system

Abstract

Provide a system. A means for receiving detailed information about an item from a user; means for receiving image data of an item from a user; means for analyzing the received image data to extract characteristics of the item; a means for integrating the extracted features with detailed information to generate an item profile; A means for searching for buyers or individuals based on the generated item profile; A means to identify the company or individual offering the highest purchase price from the search results, and a means for notifying the user of details of the identified buyer or individual; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional item buying services require users to manually request quotes from multiple vendors and find the one offering the highest price, which is time-consuming and labor-intensive. Furthermore, the accuracy of searches by vendors and individuals is low, making it difficult to find the optimal buyer. Furthermore, because buying price comparisons are done manually, there are also issues with errors and oversights. [Means for solving the problem]

[0005] The present invention provides a means for receiving detailed item information and image data from a user and analyzing the image data to extract item features. It also includes a means for integrating the extracted features and detailed information to generate an item profile and searching for buyers or individuals based on the item profile. By building a system that identifies the buyer or individual offering the highest desired purchase price from the search results and notifies the user of that detailed information, the user can efficiently find the optimal buyer. Furthermore, by including a means for analyzing image data using external services and a means for storing the detailed information and extracted features in a database, search accuracy and information management are improved.

[0006] "User" refers to an individual or corporation who wishes to purchase an item using the system.

[0007] "Detailed item information" is text data including the item's name, brand, model number, condition, features, etc.

[0008] "Image data" is a digital image file that contains visual information about an item.

[0009] A "means for extracting features" is a technological device or algorithm that analyzes image data and extracts the brand, model, condition, or other identifying characteristics of an item.

[0010] An "item profile" is a data set that combines detailed information provided by the user with features extracted from image data.

[0011] "Buyer or Individual" refers to a business or individual who is willing to purchase an item, and whose information is stored in a database.

[0012] "Searching means" means the technical means for querying a database of buyers or individuals based on an item profile to find matching entries.

[0013] The "highest desired purchase price" is the highest price offered by potential buyers.

[0014] "External services" refer to web services or APIs for analysis or data processing provided by third parties independent of the server.

[0015] The "database" is an information repository for storing and managing item profiles, buyer and personal information, etc.

[0016] "Means of notification" refers to the technical means used to send information to the user, including email, application notification, SMS, etc. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

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

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0038] System Overview

[0039] The present invention is a system that allows users to sell their items efficiently at the best price. By inputting detailed information about the item and uploading image data, the system automatically searches for buyers and individuals and identifies the person or business offering the highest purchase price.

[0040] Program processing description

[0041] Step 1: Accepting user input

[0042] 1. User: Uses a web form or mobile app to enter details about the item they want to sell (e.g., name, brand, model number, condition, etc.).

[0043] 2. User: Takes a picture of the item and uploads it to the system.

[0044] Step 2: Receiving and analyzing data

[0045] 1. Terminal: Packages the entered details and image data and sends them to the server.

[0046] 2. Server: Analyzes the received data and separates text data from image data.

[0047] 3. Server: Sends image data to an external generative AI service to extract item features.

[0048] Step 3: Generate an item profile

[0049] 1. Server: Based on the extracted features, it integrates them with the input details to generate an item profile.

[0050] 2. Server: Stores the item profiles in a database.

[0051] Step 4: Search and identify buyers and individuals

[0052] 1. Server: Searches a database of buyers and individuals based on the item profile.

[0053] 2. Server: Identify the company or individual offering the highest purchase price from the search results.

[0054] Step 5: Notification of results

[0055] 1. Server: Formats and sends details of identified businesses and individuals (e.g., name, contact information, resale price, etc.) to the user.

[0056] 2. Device: The data received from the server is displayed on the user's device, allowing the user to review the information.

[0057] Specific examples

[0058] Case: Selling an old digital camera

[0059] 1. User: The user opens the app to sell a "Sony α6000."

[0060] 2. User: Enter the brand "Sony", model number "α6000", condition "good", and upload a photo of the camera.

[0061] 3. Terminal: Sends input information and image data to the server.

[0062] 4. Server: Sends image data to the generative AI, which extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0063] 5. Server: Integrates the extracted features with the input information to generate item profiles and stores them in a database.

[0064] 6. Server: Searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0065] 7. Server: Provides the merchant details to the user.

[0066] 8. Terminal: The information of the vendor received from the server is displayed on the terminal, and the user contacts the vendor based on that information.

[0067] In this way, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0071] What happens: The user enters data into form fields and presses the "Submit" button.

[0072] Step 2:

[0073] Users take photos of items and upload them to the system.

[0074] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[0075] Step 3:

[0076] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0077] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[0078] Step 4:

[0079] The server analyzes the received request and separates the text data from the image data.

[0080] What happens: The server parses the request body and stores the text and image fields separately.

[0081] Step 5:

[0082] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[0083] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[0084] Step 6:

[0085] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[0086] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[0087] Step 7:

[0088] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[0089] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[0090] Step 8:

[0091] The server stores the generated item profile in a database.

[0092] Specific behavior: Inserts the item profile into the appropriate table in the database.

[0093] Step 9:

[0094] The server generates a query to search for buyers or individuals based on the item profile.

[0095] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[0096] Step 10:

[0097] The server identifies the trader or individual offering the highest purchase price from the search results.

[0098] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[0099] Step 11:

[0100] The server formats the details of the identified business or individual and notifies the user.

[0101] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[0102] Step 12:

[0103] The terminal receives the response on the user's terminal and displays detailed information about the trader or individual who is willing to buy the item at the highest price.

[0104] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[0105] Step 13:

[0106] The user proceeds with the transaction using the provided merchant or personal contact information.

[0107] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[0108] Example 1

[0109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0110] Conventional systems make it difficult for users to sell items efficiently at the best price. Specifically, users must contact multiple buyers and individuals and compare prices, which takes time and effort, and often results in the user being unable to find the best price. Therefore, there is a need for a system that allows users to easily and quickly find the buyer or individual who offers the highest purchase price.

[0111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0112] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for packaging the input detailed information and image data and transmitting the packaged data to the server, means for separating the received text data from the image data, means for transmitting the image data to an external generative AI service and extracting item features, means for integrating the extracted features and detailed information to generate an item profile, means for storing the generated item profile in a database, means for searching for buyers or individuals based on the item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, and means for notifying the user of detailed information about the identified buyer or individual. This allows users to easily and quickly find the buyer or individual offering the highest purchase price.

[0113] "User" means an individual or corporation that wishes to sell items using the system.

[0114] "Detailed item information" refers to various information entered by the user, such as the item's name, brand, model number, and condition.

[0115] "Item image data" refers to a photo of an item that a user takes and uploads to the system.

[0116] "Packaging" is the process of combining detailed information and image data of an item into a single data format (e.g., JSON format).

[0117] "Text data" is the text portion of the detailed information about an item entered by the user.

[0118] "Image data" refers to the image file uploaded as a photo of the item.

[0119] A "generative AI service" is an external artificial intelligence service that performs image analysis, etc.

[0120] "Feature extraction" means deriving information such as the attributes and condition of an item from image data.

[0121] An "item profile" is data that integrates detailed information about an item with extracted features.

[0122] A "database" is a data storage system that stores and manages item profiles, user information, etc.

[0123] A "buyer or individual" is a business or individual willing to purchase an item.

[0124] "Search" is the process of locating buyers or individuals within the database based on an item profile.

[0125] The "purchase price" is the purchase price offered by a buyer or individual for the item.

[0126] "Notification" is the act of notifying a user of specified information.

[0127] The present invention is a system that allows users to sell their items efficiently at the optimal price. This system extracts item characteristics based on detailed information and image data provided by the user, and generates an item profile. It then automatically searches for buyers and individuals based on the item profile, identifies the buyer or individual who offers the highest purchase price, and notifies the user of that information.

[0128] Hardware and software used

[0129] User terminal: A device used by a user to input information, such as a smartphone or PC. This device is built using a web form that uses HTML, CSS, and JavaScript (registered trademark), or a mobile app that uses React Native.

[0130] Server: The central system that receives input information, analyzes data, and communicates with generative AI services. Scripts are executed using Python or Node.js.

[0131] Data storage system: Databases such as Amazon RDS and MongoDB are used to store information.

[0132] Generative AI services: Google (registered trademark) Cloud Vision API and other services are used to analyze image data.

[0133] Program processing

[0134] 1. Using a web form or mobile app, users enter detailed information about the item they want to sell (such as name, brand, model number, and condition), and also take a photo of the item with a smartphone or digital camera and upload it to the system as image data.

[0135] 2. The device packages the input text data and image data in JSON format and sends it to the server via an HTTP POST request.

[0136] 3. The server receives the received data using the API gateway and separates the text data from the image data using a data parser.

[0137] 4. The server sends the image data to a generative AI service, which extracts item features (e.g., label information, tags, metadata) from the image data.

[0138] 5. The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[0139] 6. The server searches a database of buyers and individuals based on the item profile, and uses a sorting algorithm to identify the buyer or individual offering the highest price.

[0140] 7. The server compiles detailed information about the identified businesses and individuals (e.g., name, contact information, purchase price) into a JSON response and sends it to the user's device.

[0141] 8. The terminal parses the received response data and displays it visually on the user's device, allowing the user to contact the seller and sell the item.

[0142] Examples and prompts

[0143] Case: Selling an old digital camera "Sony α6000"

[0144] 1. The user opens the smartphone app, enters the brand "Sony," the model number "α6000," and the condition "good," then takes a photo with the camera and uploads it.

[0145] 2. The device compiles the input text information and image data in JSON format and sends it to the server using an HTTP POST request.

[0146] 3. The server analyzes the received data and sends the image data to a generative AI service, which extracts features such as "Sony α6000," "with optical zoom lens," and "some scratches."

[0147] 4. The server integrates the extracted features with the input information to generate an item profile and save it in the database.

[0148] 5. The server searches a database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0149] 6. The server notifies the user of the details of the identified vendor.

[0150] 7. The terminal displays the received information about the supplier, and the user contacts the supplier based on that information.

[0151] Prompt Sentence Examples

[0152] Upload an image of your Sony α6000 and enter details such as its condition and model number. Our system will search for the best buyers and notify you of the highest bidder.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] Users launch a web form or mobile app, enter details about the item they want to sell (such as name, brand, model number, and condition), and take and upload a photo of the item.

[0156] Input: Item text information, item image data

[0157] How it works: Using a smartphone or digital camera, the user enters text information, takes a photo, and uploads the image data.

[0158] Output: Input and image upload completion status on the user's device

[0159] Step 2:

[0160] The device packages the entered text information and image data in JSON format and sends it to the server via an HTTP POST request.

[0161] Input: User-entered text information, item image data

[0162] How it works: Using JavaScript and React Native, the input form data and image files are converted into JSON format and an HTTP POST request is created.

[0163] Output: Request data in JSON format, status of sending to server

[0164] Step 3:

[0165] The server receives the received data using an API gateway and begins separating and analyzing it, parsing and separating text data and image data.

[0166] Input: Request data in JSON format

[0167] How it works: Using Python and Node.js scripts, JSON data is parsed and separated into text data and image data.

[0168] Output: Separated text data, separated image data

[0169] Step 4:

[0170] The server sends the image data to a generative AI service, which extracts the item's features.

[0171] Input: Separated image data

[0172] How it works: Sends an API request to a generative AI service (e.g., Google Cloud Vision API) to perform feature extraction processing.

[0173] Output: Feature data from generative AI services (e.g., label information, tags, metadata)

[0174] Step 5:

[0175] The server integrates the extracted features with the input text information to generate an item profile.

[0176] Input: separated text data, feature data from generative AI services

[0177] Operation: Using a data structure, text data and feature data are integrated and organized into item profiles.

[0178] Output: Item profile (integrated data)

[0179] Step 6:

[0180] The server stores the generated item profile in a database.

[0181] Input: Item Profile

[0182] How it works: Inserts item profiles into a database (e.g., Amazon RDS, MongoDB) using a SQL or NoSQL query.

[0183] Output: Item profile saved in database, save status

[0184] Step 7:

[0185] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual who offers the highest desired purchase price.

[0186] Input: Item Profile

[0187] How it works: It uses SQL or NoSQL query languages ​​to search for businesses and individuals in a database, then uses a sorting algorithm to identify those offering the best buyback price.

[0188] Output: Details of the businesses and individuals offering the highest purchase prices

[0189] Step 8:

[0190] The server creates a response to notify the user of detailed information about the identified business or individual, and sends it to the terminal.

[0191] Input: Details of the company or individual offering the highest purchase price

[0192] Operation: Creates response data in JSON format and sends it to the user's device via an HTTP POST or GET request.

[0193] Output: JSON format response data, sending status

[0194] Step 9:

[0195] The terminal parses the response data received from the server and displays it visually on the user's device, allowing the user to contact the merchant based on that information.

[0196] Input: JSON format response data from the server

[0197] What it does: Parses the response data and displays it in a user interface using React Native or another UI framework, visually rendering the information for the user to see.

[0198] Output: details of the business or individual displayed to the user, and the user's actions (e.g., contact)

[0199] (Application example 1)

[0200] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] Currently, in order to sell their items at a high price, users must compare multiple buyers, which takes time and effort. It is also difficult to determine whether the purchase price is fair. In particular, extracting item features using image data requires specialized knowledge and high-performance analytical technology, which is difficult for average users. This can result in users missing out on opportunities to sell their items at the optimal price. There is a need to solve these problems.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0203] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for generating an item profile by integrating the extracted features and detailed information, means for searching for buyers or individuals based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for the user to input detailed item information and image data using a mobile device, thereby enabling the user to easily sell items at the optimal price.

[0204] A "User" is any person or entity that seeks to sell an item using the System.

[0205] "Detailed item information" refers to information such as the name, brand, model number, and condition of the item being sold.

[0206] "Item image data" refers to the photograph or image file data of the item you are selling.

[0207] "Features of an item" are specific attributes and characteristics of an item extracted by analyzing image data.

[0208] An "item profile" is a data set that integrates detailed information and characteristics of an item.

[0209] A "buyer or individual" is a business or individual willing to purchase an item from a user.

[0210] A "mobile device" is a portable information processing device such as a smartphone or tablet.

[0211] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze image data and extract features.

[0212] "Searching" is the process of searching through data in a database to find information that meets specific criteria.

[0213] A "notification" is a message or alert sent to inform a user of specific information.

[0214] System Overview

[0215] The basic function of the invention is a system that helps users sell their items efficiently at the best price. Users input detailed information about the item and upload image data, and the system automatically searches for buyers and individuals and identifies the one offering the highest purchase price.

[0216] Hardware and software used

[0217] Hardware: Smartphone (iOS or ANDROID (registered trademark)), server

[0218] Software: Python, Flask (web framework), PIL (image processing library), generative AI model API

[0219] 1. Receiving data

[0220] Using a smartphone app, users input detailed information about the items they want to sell (e.g., name, brand, model number, condition, etc.) and also take and upload photos of the items to provide image data to the system.

[0221] 2. Data Analysis

[0222] The device sends the input details and image data to a server, which then sends the image data to a generative AI model to extract specific features. This generative AI model then uses machine learning and deep learning to analyze the image.

[0223] 3. Generate Item Profiles

[0224] The server combines the extracted features and detailed information to generate an item profile, which is then stored in a database.

[0225] 4. Search for buyers and individuals

[0226] Based on the item profile, the server searches a database of buyers and individuals, and from the search results, identifies the buyer or individual offering the highest purchase price.

[0227] 5. Notification of results

[0228] The system formats and notifies the user of the details of the identified businesses and individuals, allowing the user to check the best buyback price information on the smartphone app and contact the businesses or individuals.

[0229] Example

[0230] For example, if a user wants to sell their old digital camera, a Sony α6000, they can use the system as follows:

[0231] 1. The user opens the smartphone app and enters the camera's details (brand "Sony," model number "α6000," condition "good").

[0232] 2. Take a photo with the camera and upload the image data through the app.

[0233] 3. The app sends the input information and image data to the server.

[0234] 4. The server sends the image data to a generative AI service to extract the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0235] 5. The extracted features are integrated with the input information to generate an item profile, which is then stored in the database.

[0236] 6. Based on the item profile, a database of buyers and individuals is searched to identify the buyer offering the highest purchase price.

[0237] 7. The details of the best fit supplier will be sent to the user's smartphone app.

[0238] Prompt Sentence Examples

[0239] An example of a prompt to input to a generative AI model is as follows:

[0240] "Extract item characteristics from the provided image file. These characteristics should include the item's name, brand, model number, condition, and any special notes."

[0241] This allows users to easily sell their digital cameras and other items through the system for the highest possible price.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] Using a smartphone app, users enter detailed information about the item they want to sell (e.g., name, brand, model number, condition) and upload image data of the item. Specifically, they enter the required text data (name, brand, model number, condition) into the app's form, take a photo or select an image from the gallery, and press the upload button. The entered detailed information and image data are temporarily stored in the app's local storage and then sent to the server for subsequent processing.

[0245] Step 2:

[0246] The device packages the details entered by the user and the uploaded image data and sends them to the server. The details are sent in JSON format, and the image data is sent in binary format. The server receives this data and prepares it for the next analysis process.

[0247] Step 3:

[0248] The server sends the received image data to the generative AI model API to extract the item's features. The image data is passed to the generative AI model using the prompt, "Please extract the item's features from the specified image file. The features should include the item's name, brand, model number, condition, and any special notes." During this process, the generative AI model uses machine learning and deep learning algorithms to analyze the image and return the extracted feature information (e.g., name "Sony α6000," condition "good," special notes "with optical zoom lens, some scratches").

[0249] Step 4:

[0250] The server combines the extracted feature information with the detailed information entered by the user to generate an item profile. Specifically, the feature information and detailed information are merged into a single JSON object, which is then saved in the database as the item profile. This provides a clear overall picture of the item.

[0251] Step 5:

[0252] The server searches for buyers or individuals based on the generated item profile. The server queries the buyer or individual database using the item's category and characteristics as keys to obtain a list of candidates. The server also obtains the desired purchase price for each candidate.

[0253] Step 6:

[0254] The server identifies the company or individual who offers the highest purchase price from the list of candidates. The server compares the purchase prices and extracts detailed information (e.g., name, contact information, purchase price) of the company or individual who offered the highest price. This determines the most advantageous seller for the user.

[0255] Step 7:

[0256] The server formats and sends detailed information about the identified sellers or individuals to the user's device, including the seller's or individual's name, contact information, and purchase price, allowing the user to identify the most suitable seller or individual within the app and contact them based on that information.

[0257] Through the above processing steps, users can easily sell items at the best price.

[0258] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0259] System Overview

[0260] The present invention is a system that allows users to efficiently sell their items at the optimal price. By entering detailed information about the item and uploading image data, users can use the system to automatically search for buyers and individuals and identify the person or business offering the highest purchase price. Furthermore, the present invention adds a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[0261] Program processing description

[0262] Step 1: Accepting user input

[0263] 1. A user uses a web form or mobile app to enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0264] 2. The user takes a photo of the item and uploads it to the system.

[0265] Step 2: Receiving and analyzing data

[0266] 1. The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0267] 2. The server analyzes the received request and separates the text data from the image data.

[0268] 3. The server sends the image data to an external generative AI service to extract the item's features.

[0269] Step 3: Generate an item profile

[0270] 1. The server generates an item profile based on the extracted features and integrates them with the input details.

[0271] 2. The server stores the item profile in a database.

[0272] Step 4: Search and identify buyers and individuals

[0273] 1. The server searches a database of buyers and individuals based on the item profile.

[0274] 2. The server identifies the business or individual offering the highest purchase price from the search results.

[0275] Step 5: Use the Emotion Engine

[0276] 1. The server launches the emotion engine to recognize the user's emotional state.

[0277] 2. The emotion engine analyzes user input and behavioral data to assess the user's current emotional state.

[0278] 3. The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[0279] Step 6: Notification of results

[0280] 1. The server formats the details of the identified merchant or individual (such as name, contact information, and offered price) and notifies the user.

[0281] 2. The terminal displays the data received from the server on the user's device so that the user can check the information.

[0282] Specific examples

[0283] Case: Selling old digital cameras and emotion recognition

[0284] 1. A user opens the app to sell a Sony a6000.

[0285] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[0286] 3. The terminal sends the input information and image data to the server.

[0287] 4. The server sends the image data to a generative AI that extracts the item's characteristics (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0288] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[0289] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[0290] 7. The server utilizes an emotion engine to assess the user's emotional state.

[0291] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[0292] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[0293] 10. The server formats the identified vendor information and notifies the user.

[0294] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[0295] In this way, by using the system based on the present invention, users can easily find businesses or individuals that will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0299] What happens: The user enters data into form fields and presses the "Submit" button.

[0300] Step 2:

[0301] Users take photos of items and upload them to the system.

[0302] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[0303] Step 3:

[0304] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0305] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[0306] Step 4:

[0307] The server analyzes the received request and separates the text data from the image data.

[0308] What happens: The server parses the request body and stores the text and image fields separately.

[0309] Step 5:

[0310] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[0311] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[0312] Step 6:

[0313] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[0314] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[0315] Step 7:

[0316] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[0317] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[0318] Step 8:

[0319] The server stores the generated item profile in a database.

[0320] Specific behavior: Inserts the item profile into the appropriate table in the database.

[0321] Step 9:

[0322] The server generates a query to search for buyers or individuals based on the item profile.

[0323] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[0324] Step 10:

[0325] The server identifies the trader or individual offering the highest purchase price from the search results.

[0326] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[0327] Step 11:

[0328] The server activates an emotion engine to recognize the user's emotional state.

[0329] Specific operation: Sends user input data and behavioral data to the emotion engine and requests emotion analysis.

[0330] Step 12:

[0331] The emotion engine analyzes the user's emotional state and returns the results to the server.

[0332] Specific operation: The emotion engine analyzes the user's text input and behavioral data, generates emotion labels such as "satisfied" or "anxiety," and returns them to the server.

[0333] Step 13:

[0334] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[0335] Specific operation: An algorithm is applied that dynamically changes notification messages and suggestions depending on the user's emotional state.

[0336] Step 14:

[0337] The server formats the details of the identified business or individual and notifies the user.

[0338] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[0339] Step 15:

[0340] The terminal displays the data received from the server on the user's device, displaying detailed information about businesses and individuals willing to buy the items at the highest price.

[0341] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[0342] Step 16:

[0343] The user contacts businesses and individuals based on the information provided.

[0344] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[0345] Example 2

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

[0347] Currently, systems that allow users to sell items efficiently and at optimal prices require simple and fast procedures for data entry and searching for buyers. However, conventional systems have had difficulty automating these procedures and providing feedback that reflects the user's emotional state. Furthermore, they often lack functionality to enhance user satisfaction throughout the entire selling process.

[0348] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for a buyer or individual based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for recognizing the user's emotional state and adjusting the notification content and suggestions. This allows the user to efficiently sell items at the highest price, and furthermore, by receiving feedback based on emotional recognition, the user can achieve a better user experience and greater satisfaction.

[0349] "User" means an individual or corporation that wishes to sell items using the system.

[0350] "Detailed item information" refers to attribute information such as the name, brand, model number, and condition of the item being sold.

[0351] "Item image data" refers to a photo or image file of the item being sold.

[0352] A "generative AI model" is an algorithm or system that extracts specific patterns or features from data provided to it.

[0353] A "prompt sentence" is an input sentence that instructs an AI model for a specific purpose.

[0354] "Item features" refers to information about the appearance and condition of an item extracted through image analysis.

[0355] An "item profile" is a data set that integrates detailed information and feature information about an item.

[0356] A "buyer or individual" is a business or individual who is willing to buy items that a user wishes to sell.

[0357] An "emotion engine" is a software module for analyzing and assessing a user's emotional state.

[0358] The "means for adjusting notification content and suggestions" is a mechanism that has the function of dynamically changing notification content and suggestions based on the user's emotional state.

[0359] The present invention is a system that allows users to efficiently sell their items at the optimal price. The system automatically searches for buyers and individuals by allowing users to input detailed information about the items and upload image data, and identifies the person or seller offering the highest purchase price. The system also has a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[0360] Hardware and software used

[0361] Hardware

[0362] 1. Device: The smartphone, tablet, or computer used by the user.

[0363] 2. Server: A cloud server for data analysis, search, notifications, etc.

[0364] software

[0365] 1. Web form or mobile app: An interface for users to enter and upload item details and image data.

[0366] 2. Generative AI model: Used to extract features from image data of items. Specifically, Azure® Computer Vision API and Google Cloud Vision API are examples.

[0367] 3. Emotion engine: Software for analyzing the user's emotional state, such as Amazon Rekognition or Microsoft® Azure Emotion API.

[0368] 4. Database: A storage system for storing detailed item information, image data, extracted features, item profiles, buyer information, etc.

[0369] Data processing and calculation

[0370] Data Entry

[0371] Users access the system using a terminal, enter details of the item they want to sell (such as name, brand, model number, and condition) through a web form or mobile app, and take photos of the item using a smartphone or camera and upload them to the system.

[0372] Data Packaging and Transmission

[0373] The device packages the entered details and image data. This package contains text data in JSON format and Base64-encoded image data. This data is sent to the server via an HTTP POST request.

[0374] Data analysis

[0375] The server analyzes the received package. First, it parses the JSON data to separate it into text data and image data. Next, it creates a prompt to send to the generative AI model. An example of a specific prompt would be, "Please extract the features of the item shown in this image."

[0376] Item Profile Generation

[0377] The server receives the feature information returned by the generative AI model (e.g., "Sony α6000," "with optical zoom lens," "some scratches") and combines it with the detailed information entered by the user to generate an item profile, which is then stored in an SQL database.

[0378] Search and identify buyers and individuals

[0379] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual offering the highest purchase price. The search is performed using SQL or NoSQL queries.

[0380] Emotional state analysis

[0381] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavioral data, such as keystroke speed and microexpressions, to evaluate the user's current emotional state. Based on the evaluation results, the notification content and suggestions are adjusted. For example, the content may be adjusted to say, "Don't worry. We've found the best buy price for you."

[0382] Notification of results

[0383] The server formats and notifies the user of the details of the identified merchant or individual (such as name, contact information, and offered price). This data is sent to the user's device in HTML or JSON format. The device displays the data received from the server on the user's device, allowing the user to review the information and contact the merchant if necessary.

[0384] Specific examples

[0385] Case: Selling old digital cameras and emotion recognition

[0386] 1. A user opens the app to sell a Sony a6000.

[0387] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[0388] 3. The terminal sends the input information and image data to the server.

[0389] 4. The server sends the image data to a generative AI that extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0390] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[0391] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[0392] 7. The server utilizes an emotion engine to assess the user's emotional state.

[0393] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[0394] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., "Congratulations, you found the best buy price!" if a happy emotion is recognized).

[0395] 10. The server formats the identified vendor information and notifies the user.

[0396] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[0397] As described above, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[0398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0399] Step 1: Receiving User Input

[0400] Users access the system using a web form or mobile app. They enter details of the item they want to sell (such as name, brand, model number, and condition), and they also take a photo of the item and upload the image data to the system.

[0401] Input: Item details, image data

[0402] Output: User input data (details and image data)

[0403] Step 2: Packaging and sending the data

[0404] The device packages the detailed information and image data entered by the user. This package contains text data in JSON format and Base64-encoded image data. The device sends this package to the server via an HTTP POST request.

[0405] Input: User-entered data (details and image data)

[0406] Output: HTTP POST request (details in JSON format and Base64 encoded image data)

[0407] Step 3: Receiving and analyzing data

[0408] The server analyzes the received HTTP POST request. It parses the JSON data to separate the text data from the image data. It then creates a prompt to analyze the image data. The specific prompt is "Please extract the characteristics of the item shown in this image."

[0409] Input: HTTP POST request (detailed information in JSON format and Base64 encoded image data)

[0410] Output: Text data, image data, prompt

[0411] Step 4: Analyzing the image data

[0412] The server sends the image data to a generative AI model, which extracts the item's features from the image using the Azure Computer Vision API or Google Cloud Vision API.

[0413] Input: Image data, prompt ("Please extract the features of the item in this image.")

[0414] Output: Characteristic information (e.g. "Sony α6000", "with optical zoom lens", "some scratches")

[0415] Step 5: Generate item profiles

[0416] The server combines the feature information returned by the generative AI model with the details entered by the user to generate an item profile, which is then stored in an SQL database.

[0417] Input: characteristic information, detailed information

[0418] Output: Item Profiles (records stored in a SQL database)

[0419] Step 6: Search the database of buyers and individuals

[0420] The server searches a database of buyers and individuals based on the generated item profile. This search uses SQL or NoSQL queries to identify the buyer or individual offering the highest purchase price.

[0421] Input: Item Profile

[0422] Output: List of buyers and individuals, the buyer or individual who offers the highest purchase price

[0423] Step 7: Analyze emotional state

[0424] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavior data and evaluates the user's current emotional state.

[0425] Input: User input data, behavioral data

[0426] Output: Emotion evaluation result (e.g., "Satisfied" or "Anxious")

[0427] Step 8: Adjust your notifications

[0428] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine. For example, if the user is rated as "anxious," the content will be adjusted to something like "Don't worry. We've found the best buy price for you."

[0429] Input: Emotion evaluation result

[0430] Output: Adjusted notification content

[0431] Step 9: Notification of results

[0432] The server formats and transmits the details of the identified merchant or individual (such as name, contact details, and offered price) to the user's terminal.

[0433] Input: Business or individual details, tailored notification content

[0434] Output: A message to inform the user

[0435] Step 10: Display to the User

[0436] The terminal displays the data received from the server on the user's device, allowing the user to contact a seller and sell the item at the highest price possible.

[0437] Input: A notification message to the user

[0438] Output: Displayed notification content

[0439] The above is the specific flow of program processing in the system of the present invention.

[0440] (Application example 2)

[0441] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0442] In conventional item selling systems, users had to spend a lot of time and effort to find the most suitable buyer. Furthermore, because notifications and suggestions were made uniformly without taking the user's emotional state into consideration, it was difficult to improve the user experience. Furthermore, when analyzing image data, it was difficult to extract features with high accuracy, and accurate evaluation of items was often impossible. This reduced the reliability and usability of the system.

[0443] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0444] In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for potential buyers based on the generated item profile, means for identifying the potential buyer offering the highest desired purchase price from the search results, means for notifying the user of detailed information of the identified potential buyer, and means for recognizing the user's emotional state and adjusting the notification content and proposals based on that state. This allows the user to efficiently find the most suitable potential buyer, and the user experience is improved by responding in consideration of emotions.

[0445] A "user" is someone who uses this system to provide detailed information and image data about an item and find the most suitable buyer.

[0446] "Detailed item information" refers to specific information about the item being sold, such as the name, brand, model number, and condition.

[0447] "Image data" refers to photographs or images of the items being sold, which visually indicate the condition and characteristics of the items.

[0448] "Item characteristics" refers to the characteristics and attributes related to the appearance and condition of an item that are obtained by analyzing image data.

[0449] An "item profile" is a comprehensive description of an item that is generated by integrating user-supplied details with analyzed image data features.

[0450] "Purchaser" refers to a purchasing company or individual who wishes to purchase an item.

[0451] "Emotional state" refers to the emotional state that a user shows while using the system, and includes emotional expressions such as joy and anxiety.

[0452] "Notification content" refers to the method and message used to convey to the user detailed information about the most suitable buyer and the desired purchase price.

[0453] "Suggestions" refer to specific actions or information that are based on the user's emotional state and are intended to improve the user experience.

[0454] The present invention provides a system that allows users to sell their items efficiently at optimal prices. The system improves the user experience by recognizing the user's emotional state and adjusting notifications and suggestions accordingly. Specific embodiments of the present invention are described below.

[0455] System Configuration

[0456] This system mainly consists of the following components:

[0457] 1. User device: The device used by the user, such as a smartphone or computer.

[0458] 2. Server: A central system that processes information and connects to a database of potential buyers.

[0459] 3. Generative AI services: External services for analyzing image data, such as Google Cloud Vision API or IBM Watson® Visual Recognition.

[0460] 4. Emotion recognition engine: Software for assessing the user's emotional state, such as the Affectiva SDK or Microsoft Azure Emotion API.

[0461] Program processing flow

[0462] Receiving User Input

[0463] The user inputs detailed information (such as name, brand, model number, and condition) of the item they wish to sell using a user terminal. They also take a picture of the item and upload it to the system.

[0464] Data reception and analysis

[0465] The user device sends the entered detailed information and image data to the server. The server analyzes the data and processes the text and image data. The image data is sent to the generation AI service, which extracts the item's features.

[0466] Generate an item profile

[0467] The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[0468] Searching for and identifying potential buyers

[0469] The server searches a database of potential buyers based on the item profile and identifies the potential buyer offering the highest desired purchase price.

[0470] Use of emotion engine

[0471] The server activates an emotion recognition engine to recognize the user's emotional state. It analyzes the user's input and behavior data and evaluates the emotional state.

[0472] Notification of results

[0473] The server notifies the user of the details of the identified potential buyers (such as their names, contact information, and asking prices). The content of the notification is adjusted based on the evaluation results from the emotion recognition engine.

[0474] Specific examples

[0475] For example, if a user wants to sell an Apple iPhone (registered trademark) 12, the system operates as follows:

[0476] 1. The user enters "iPhone 12, Brand: Apple, Model: A2172, Condition: Good" and uploads a photo of the item.

[0477] 2. The user terminal sends the input information and image data to the server.

[0478] 3. The server analyzes the image using the Google Cloud Vision API and extracts features such as "iPhone 12," "no scratches," and "good working condition."

[0479] 4. The server integrates the extracted features and detailed information to generate an item profile and stores it in a database.

[0480] 5. The server searches the database of potential buyers based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0481] 6. The emotion engine assesses the user's emotional state and recognizes emotions such as joy and anxiety.

[0482] 7. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[0483] 8. The server formats the details of the identified buyers and notifies the user.

[0484] Prompt Sentence Examples

[0485] Please tell us the name of the item you are trying to sell.

[0486] "Please enter the item brand and model number"

[0487] "What is the current condition of the item (new, good condition, used, etc.)?"

[0488] Upload a photo of the item

[0489] In this way, users can efficiently find the best buyers, and emotional responses provide a better user experience.

[0490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0491] Step 1:

[0492] The user enters and uploads detailed information and image data for the item.

[0493] Using a user device (smartphone or PC), the user inputs detailed information about the item they want to sell, such as the name, brand, model number, and condition, and then takes or selects and uploads a photo of the item. Detailed information and image data are obtained as user input. This information is output as a temporary save on the device.

[0494] Step 2:

[0495] The device sends the input information and image data to the server.

[0496] The terminal packages the details and image data entered by the user and sends it to the server via an HTTP POST request. The input is the details and image data entered by the user, and the output is the transmission to the server.

[0497] Step 3:

[0498] The server receives and analyzes the data

[0499] The server receives data sent from the device and separates it into text data and image data. The image data is sent to an external AI generation service (Google Cloud Vision API or IBM Watson Visual Recognition) to extract its features. The input is data from the device, and the output is separated text data and image data with extracted features.

[0500] Step 4:

[0501] The server generates and saves the item profile.

[0502] The server integrates the extracted features with the text data to generate an item profile, which is then stored in a database. The input is the separated text data and extracted features, and the output is the item profile.

[0503] Step 5:

[0504] The server searches and identifies potential buyers

[0505] The server searches the database of potential buyers based on the item profile stored in the database. It identifies the potential buyer who offers the highest purchase price. The input is the item profile, and the output is the details of the most suitable potential buyer.

[0506] Step 6:

[0507] The server uses an emotion engine to evaluate the user's emotional state.

[0508] The server uses an emotion recognition engine (Affectiva SDK or Microsoft Azure Emotion API) to analyze the user's input and behavioral data and evaluate their emotional state. The input is the user's behavioral data, and the output is the evaluated user's emotional state.

[0509] Step 7:

[0510] The server adjusts the notification content and notifies the user.

[0511] The server adjusts the notification content and proposals based on the evaluation results and notifies the user of the details of the identified potential buyers. The notification content is adjusted based on the user's emotional state. The input is the details of the identified potential buyers and the evaluated emotional state, and the output is a notification message.

[0512] Step 8:

[0513] The device displays the notification from the server to the user.

[0514] The terminal displays the notification message received from the server to the user. The user can contact the potential buyer based on this information. The input is the notification message from the server, and the output is the display to the user.

[0515] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0516] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0517] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0521] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0522] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0523] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0524] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0525] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0526] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0527] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0528] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0529] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0530] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0531] System Overview

[0532] The present invention is a system that allows users to sell their items efficiently at the best price. By inputting detailed information about the item and uploading image data, the system automatically searches for buyers and individuals and identifies the person or business offering the highest purchase price.

[0533] Program processing description

[0534] Step 1: Accepting user input

[0535] 1. User: Uses a web form or mobile app to enter details about the item they want to sell (e.g., name, brand, model number, condition, etc.).

[0536] 2. User: Takes a picture of the item and uploads it to the system.

[0537] Step 2: Receiving and analyzing data

[0538] 1. Terminal: Packages the entered details and image data and sends them to the server.

[0539] 2. Server: Analyzes the received data and separates text data from image data.

[0540] 3. Server: Sends image data to an external generative AI service to extract item features.

[0541] Step 3: Generate an item profile

[0542] 1. Server: Based on the extracted features, it integrates them with the input details to generate an item profile.

[0543] 2. Server: Stores the item profiles in a database.

[0544] Step 4: Search and identify buyers and individuals

[0545] 1. Server: Searches a database of buyers and individuals based on the item profile.

[0546] 2. Server: Identify the company or individual offering the highest purchase price from the search results.

[0547] Step 5: Notification of results

[0548] 1. Server: Formats and sends details of identified businesses and individuals (e.g., name, contact information, resale price, etc.) to the user.

[0549] 2. Device: The data received from the server is displayed on the user's device, allowing the user to review the information.

[0550] Specific examples

[0551] Case: Selling an old digital camera

[0552] 1. User: The user opens the app to sell a "Sony α6000."

[0553] 2. User: Enter the brand "Sony", model number "α6000", condition "good", and upload a photo of the camera.

[0554] 3. Terminal: Sends input information and image data to the server.

[0555] 4. Server: Sends image data to the generative AI, which extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0556] 5. Server: Integrates the extracted features with the input information to generate item profiles and stores them in a database.

[0557] 6. Server: Searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0558] 7. Server: Provides the merchant details to the user.

[0559] 8. Terminal: The information of the vendor received from the server is displayed on the terminal, and the user contacts the vendor based on that information.

[0560] In this way, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price.

[0561] The processing flow will be explained below.

[0562] Step 1:

[0563] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0564] What happens: The user enters data into form fields and presses the "Submit" button.

[0565] Step 2:

[0566] Users take photos of items and upload them to the system.

[0567] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[0568] Step 3:

[0569] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0570] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[0571] Step 4:

[0572] The server analyzes the received request and separates the text data from the image data.

[0573] What happens: The server parses the request body and stores the text and image fields separately.

[0574] Step 5:

[0575] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[0576] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[0577] Step 6:

[0578] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[0579] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[0580] Step 7:

[0581] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[0582] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[0583] Step 8:

[0584] The server stores the generated item profile in a database.

[0585] Specific behavior: Inserts the item profile into the appropriate table in the database.

[0586] Step 9:

[0587] The server generates a query to search for buyers or individuals based on the item profile.

[0588] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[0589] Step 10:

[0590] The server identifies the trader or individual offering the highest purchase price from the search results.

[0591] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[0592] Step 11:

[0593] The server formats the details of the identified business or individual and notifies the user.

[0594] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[0595] Step 12:

[0596] The terminal receives the response on the user's terminal and displays detailed information about the trader or individual who is willing to buy the item at the highest price.

[0597] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[0598] Step 13:

[0599] The user proceeds with the transaction using the provided merchant or personal contact information.

[0600] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[0601] Example 1

[0602] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Conventional systems make it difficult for users to sell items efficiently at the best price. Specifically, users must contact multiple buyers and individuals and compare prices, which takes time and effort, and often results in the user being unable to find the best price. Therefore, there is a need for a system that allows users to easily and quickly find the buyer or individual who offers the highest purchase price.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0605] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for packaging the input detailed information and image data and transmitting the packaged data to the server, means for separating the received text data from the image data, means for transmitting the image data to an external generative AI service and extracting item features, means for integrating the extracted features and detailed information to generate an item profile, means for storing the generated item profile in a database, means for searching for buyers or individuals based on the item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, and means for notifying the user of detailed information about the identified buyer or individual. This allows users to easily and quickly find the buyer or individual offering the highest purchase price.

[0606] "User" means an individual or corporation that wishes to sell items using the system.

[0607] "Detailed item information" refers to various information entered by the user, such as the item's name, brand, model number, and condition.

[0608] "Item image data" refers to a photo of an item that a user takes and uploads to the system.

[0609] "Packaging" is the process of combining detailed information and image data of an item into a single data format (e.g., JSON format).

[0610] "Text data" is the text portion of the detailed information about an item entered by the user.

[0611] "Image data" refers to the image file uploaded as a photo of the item.

[0612] A "generative AI service" is an external artificial intelligence service that performs image analysis, etc.

[0613] "Feature extraction" means deriving information such as the attributes and condition of an item from image data.

[0614] An "item profile" is data that integrates detailed information about an item with extracted features.

[0615] A "database" is a data storage system that stores and manages item profiles, user information, etc.

[0616] A "buyer or individual" is a business or individual willing to purchase an item.

[0617] "Search" is the process of locating buyers or individuals within the database based on an item profile.

[0618] The "purchase price" is the purchase price offered by a buyer or individual for the item.

[0619] "Notification" is the act of notifying a user of specified information.

[0620] The present invention is a system that allows users to sell their items efficiently at the optimal price. This system extracts item characteristics based on detailed information and image data provided by the user, and generates an item profile. It then automatically searches for buyers and individuals based on the item profile, identifies the buyer or individual who offers the highest purchase price, and notifies the user of that information.

[0621] Hardware and software used

[0622] User device: A device such as a smartphone or PC where a user inputs information. This device is built using web forms using HTML, CSS, and JavaScript, or a mobile app using React Native.

[0623] Server: The central system that receives input information, analyzes data, and communicates with generative AI services. Scripts are executed using Python or Node.js.

[0624] Data storage system: Databases such as Amazon RDS and MongoDB are used to store information.

[0625] Generative AI services: Google Cloud Vision API and other services are used to analyze image data.

[0626] Program processing

[0627] 1. Using a web form or mobile app, users enter detailed information about the item they want to sell (such as name, brand, model number, and condition), and also take a photo of the item with a smartphone or digital camera and upload it to the system as image data.

[0628] 2. The device packages the input text data and image data in JSON format and sends it to the server via an HTTP POST request.

[0629] 3. The server receives the received data using the API gateway and separates the text data from the image data using a data parser.

[0630] 4. The server sends the image data to a generative AI service, which extracts item features (e.g., label information, tags, metadata) from the image data.

[0631] 5. The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[0632] 6. The server searches a database of buyers and individuals based on the item profile, and uses a sorting algorithm to identify the buyer or individual offering the highest price.

[0633] 7. The server compiles detailed information about the identified businesses and individuals (e.g., name, contact information, purchase price) into a JSON response and sends it to the user's device.

[0634] 8. The terminal parses the received response data and displays it visually on the user's device, allowing the user to contact the seller and sell the item.

[0635] Examples and prompts

[0636] Case: Selling an old digital camera "Sony α6000"

[0637] 1. The user opens the smartphone app, enters the brand "Sony," the model number "α6000," and the condition "good," then takes a photo with the camera and uploads it.

[0638] 2. The device compiles the input text information and image data in JSON format and sends it to the server using an HTTP POST request.

[0639] 3. The server analyzes the received data and sends the image data to a generative AI service, which extracts features such as "Sony α6000," "with optical zoom lens," and "some scratches."

[0640] 4. The server integrates the extracted features with the input information to generate an item profile and save it in the database.

[0641] 5. The server searches a database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0642] 6. The server notifies the user of the details of the identified vendor.

[0643] 7. The terminal displays the received information about the supplier, and the user contacts the supplier based on that information.

[0644] Prompt Sentence Examples

[0645] Upload an image of your Sony α6000 and enter details such as its condition and model number. Our system will search for the best buyers and notify you of the highest bidder.

[0646] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] Users launch a web form or mobile app, enter details about the item they want to sell (such as name, brand, model number, and condition), and take and upload a photo of the item.

[0649] Input: Item text information, item image data

[0650] How it works: Using a smartphone or digital camera, the user enters text information, takes a photo, and uploads the image data.

[0651] Output: Input and image upload completion status on the user's device

[0652] Step 2:

[0653] The device packages the entered text information and image data in JSON format and sends it to the server via an HTTP POST request.

[0654] Input: User-entered text information, item image data

[0655] How it works: Using JavaScript and React Native, the input form data and image files are converted into JSON format and an HTTP POST request is created.

[0656] Output: Request data in JSON format, status of sending to server

[0657] Step 3:

[0658] The server receives the received data using an API gateway and begins separating and analyzing it, parsing and separating text data and image data.

[0659] Input: Request data in JSON format

[0660] How it works: Using Python and Node.js scripts, JSON data is parsed and separated into text data and image data.

[0661] Output: Separated text data, separated image data

[0662] Step 4:

[0663] The server sends the image data to a generative AI service, which extracts the item's features.

[0664] Input: Separated image data

[0665] How it works: Sends an API request to a generative AI service (e.g., Google Cloud Vision API) to perform feature extraction processing.

[0666] Output: Feature data from generative AI services (e.g., label information, tags, metadata)

[0667] Step 5:

[0668] The server integrates the extracted features with the input text information to generate an item profile.

[0669] Input: separated text data, feature data from generative AI services

[0670] Operation: Using a data structure, text data and feature data are integrated and organized into item profiles.

[0671] Output: Item profile (integrated data)

[0672] Step 6:

[0673] The server stores the generated item profile in a database.

[0674] Input: Item Profile

[0675] How it works: Inserts item profiles into a database (e.g., Amazon RDS, MongoDB) using a SQL or NoSQL query.

[0676] Output: Item profile saved in database, save status

[0677] Step 7:

[0678] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual who offers the highest desired purchase price.

[0679] Input: Item Profile

[0680] How it works: It uses SQL or NoSQL query languages ​​to search for businesses and individuals in a database, then uses a sorting algorithm to identify those offering the best buyback price.

[0681] Output: Details of the businesses and individuals offering the highest purchase prices

[0682] Step 8:

[0683] The server creates a response to notify the user of detailed information about the identified business or individual, and sends it to the terminal.

[0684] Input: Details of the company or individual offering the highest purchase price

[0685] Operation: Creates response data in JSON format and sends it to the user's device via an HTTP POST or GET request.

[0686] Output: JSON format response data, sending status

[0687] Step 9:

[0688] The terminal parses the response data received from the server and displays it visually on the user's device, allowing the user to contact the merchant based on that information.

[0689] Input: JSON format response data from the server

[0690] What it does: Parses the response data and displays it in a user interface using React Native or another UI framework, visually rendering the information for the user to see.

[0691] Output: details of the business or individual displayed to the user, and the user's actions (e.g., contact)

[0692] (Application example 1)

[0693] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0694] Currently, in order to sell their items at a high price, users must compare multiple buyers, which takes time and effort. It is also difficult to determine whether the purchase price is fair. In particular, extracting item features using image data requires specialized knowledge and high-performance analytical technology, which is difficult for average users. This can result in users missing out on opportunities to sell their items at the optimal price. There is a need to solve these problems.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0696] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for generating an item profile by integrating the extracted features and detailed information, means for searching for buyers or individuals based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for the user to input detailed item information and image data using a mobile device, thereby enabling the user to easily sell items at the optimal price.

[0697] A "User" is any person or entity that seeks to sell an item using the System.

[0698] "Detailed item information" refers to information such as the name, brand, model number, and condition of the item being sold.

[0699] "Item image data" refers to the photograph or image file data of the item you are selling.

[0700] "Features of an item" are specific attributes and characteristics of an item extracted by analyzing image data.

[0701] An "item profile" is a data set that integrates detailed information and characteristics of an item.

[0702] A "buyer or individual" is a business or individual willing to purchase an item from a user.

[0703] A "mobile device" is a portable information processing device such as a smartphone or tablet.

[0704] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze image data and extract features.

[0705] "Searching" is the process of searching through data in a database to find information that meets specific criteria.

[0706] A "notification" is a message or alert sent to inform a user of specific information.

[0707] System Overview

[0708] The basic function of the invention is a system that helps users sell their items efficiently at the best price. Users input detailed information about the item and upload image data, and the system automatically searches for buyers and individuals and identifies the one offering the highest purchase price.

[0709] Hardware and software used

[0710] Hardware: Smartphone (iOS or Android), server

[0711] Software: Python, Flask (web framework), PIL (image processing library), generative AI model API

[0712] 1. Receiving data

[0713] Using a smartphone app, users input detailed information about the items they want to sell (e.g., name, brand, model number, condition, etc.) and also take and upload photos of the items to provide image data to the system.

[0714] 2. Data Analysis

[0715] The device sends the input details and image data to a server, which then sends the image data to a generative AI model to extract specific features. This generative AI model then uses machine learning and deep learning to analyze the image.

[0716] 3. Generate Item Profiles

[0717] The server combines the extracted features and detailed information to generate an item profile, which is then stored in a database.

[0718] 4. Search for buyers and individuals

[0719] Based on the item profile, the server searches a database of buyers and individuals, and from the search results, identifies the buyer or individual offering the highest purchase price.

[0720] 5. Notification of results

[0721] The system formats and notifies the user of the details of the identified businesses and individuals, allowing the user to check the best buyback price information on the smartphone app and contact the businesses or individuals.

[0722] Example

[0723] For example, if a user wants to sell their old digital camera, a Sony α6000, they can use the system as follows:

[0724] 1. The user opens the smartphone app and enters the camera's details (brand "Sony," model number "α6000," condition "good").

[0725] 2. Take a photo with the camera and upload the image data through the app.

[0726] 3. The app sends the input information and image data to the server.

[0727] 4. The server sends the image data to a generative AI service to extract the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0728] 5. The extracted features are integrated with the input information to generate an item profile, which is then stored in the database.

[0729] 6. Based on the item profile, a database of buyers and individuals is searched to identify the buyer offering the highest purchase price.

[0730] 7. The details of the best fit supplier will be sent to the user's smartphone app.

[0731] Prompt Sentence Examples

[0732] An example of a prompt to input to a generative AI model is as follows:

[0733] "Extract item characteristics from the provided image file. These characteristics should include the item's name, brand, model number, condition, and any special notes."

[0734] This allows users to easily sell their digital cameras and other items through the system for the highest possible price.

[0735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0736] Step 1:

[0737] Using a smartphone app, users enter detailed information about the item they want to sell (e.g., name, brand, model number, condition) and upload image data of the item. Specifically, they enter the required text data (name, brand, model number, condition) into the app's form, take a photo or select an image from the gallery, and press the upload button. The entered detailed information and image data are temporarily stored in the app's local storage and then sent to the server for subsequent processing.

[0738] Step 2:

[0739] The device packages the details entered by the user and the uploaded image data and sends them to the server. The details are sent in JSON format, and the image data is sent in binary format. The server receives this data and prepares it for the next analysis process.

[0740] Step 3:

[0741] The server sends the received image data to the generative AI model API to extract the item's features. The image data is passed to the generative AI model using the prompt, "Please extract the item's features from the specified image file. The features should include the item's name, brand, model number, condition, and any special notes." During this process, the generative AI model uses machine learning and deep learning algorithms to analyze the image and return the extracted feature information (e.g., name "Sony α6000," condition "good," special notes "with optical zoom lens, some scratches").

[0742] Step 4:

[0743] The server combines the extracted feature information with the detailed information entered by the user to generate an item profile. Specifically, the feature information and detailed information are merged into a single JSON object, which is then saved in the database as the item profile. This provides a clear overall picture of the item.

[0744] Step 5:

[0745] The server searches for buyers or individuals based on the generated item profile. The server queries the buyer or individual database using the item's category and characteristics as keys to obtain a list of candidates. The server also obtains the desired purchase price for each candidate.

[0746] Step 6:

[0747] The server identifies the company or individual who offers the highest purchase price from the list of candidates. The server compares the purchase prices and extracts detailed information (e.g., name, contact information, purchase price) of the company or individual who offered the highest price. This determines the most advantageous seller for the user.

[0748] Step 7:

[0749] The server formats and sends detailed information about the identified sellers or individuals to the user's device, including the seller's or individual's name, contact information, and purchase price, allowing the user to identify the most suitable seller or individual within the app and contact them based on that information.

[0750] Through the above processing steps, users can easily sell items at the best price.

[0751] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0752] System Overview

[0753] The present invention is a system that allows users to efficiently sell their items at the optimal price. By entering detailed information about the item and uploading image data, users can use the system to automatically search for buyers and individuals and identify the person or business offering the highest purchase price. Furthermore, the present invention adds a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[0754] Program processing description

[0755] Step 1: Accepting user input

[0756] 1. A user uses a web form or mobile app to enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0757] 2. The user takes a photo of the item and uploads it to the system.

[0758] Step 2: Receiving and analyzing data

[0759] 1. The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0760] 2. The server analyzes the received request and separates the text data from the image data.

[0761] 3. The server sends the image data to an external generative AI service to extract the item's features.

[0762] Step 3: Generate an item profile

[0763] 1. The server generates an item profile based on the extracted features and integrates them with the input details.

[0764] 2. The server stores the item profile in a database.

[0765] Step 4: Search and identify buyers and individuals

[0766] 1. The server searches a database of buyers and individuals based on the item profile.

[0767] 2. The server identifies the business or individual offering the highest purchase price from the search results.

[0768] Step 5: Use the Emotion Engine

[0769] 1. The server launches the emotion engine to recognize the user's emotional state.

[0770] 2. The emotion engine analyzes user input and behavioral data to assess the user's current emotional state.

[0771] 3. The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[0772] Step 6: Notification of results

[0773] 1. The server formats the details of the identified merchant or individual (such as name, contact information, and offered price) and notifies the user.

[0774] 2. The terminal displays the data received from the server on the user's device so that the user can check the information.

[0775] Specific examples

[0776] Case: Selling old digital cameras and emotion recognition

[0777] 1. A user opens the app to sell a Sony a6000.

[0778] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[0779] 3. The terminal sends the input information and image data to the server.

[0780] 4. The server sends the image data to a generative AI that extracts the item's characteristics (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0781] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[0782] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[0783] 7. The server utilizes an emotion engine to assess the user's emotional state.

[0784] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[0785] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[0786] 10. The server formats the identified vendor information and notifies the user.

[0787] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[0788] In this way, by using the system based on the present invention, users can easily find businesses or individuals that will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[0789] The processing flow will be explained below.

[0790] Step 1:

[0791] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[0792] What happens: The user enters data into form fields and presses the "Submit" button.

[0793] Step 2:

[0794] Users take photos of items and upload them to the system.

[0795] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[0796] Step 3:

[0797] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[0798] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[0799] Step 4:

[0800] The server analyzes the received request and separates the text data from the image data.

[0801] What happens: The server parses the request body and stores the text and image fields separately.

[0802] Step 5:

[0803] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[0804] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[0805] Step 6:

[0806] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[0807] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[0808] Step 7:

[0809] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[0810] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[0811] Step 8:

[0812] The server stores the generated item profile in a database.

[0813] Specific behavior: Inserts the item profile into the appropriate table in the database.

[0814] Step 9:

[0815] The server generates a query to search for buyers or individuals based on the item profile.

[0816] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[0817] Step 10:

[0818] The server identifies the trader or individual offering the highest purchase price from the search results.

[0819] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[0820] Step 11:

[0821] The server activates an emotion engine to recognize the user's emotional state.

[0822] Specific operation: Sends user input data and behavioral data to the emotion engine and requests emotion analysis.

[0823] Step 12:

[0824] The emotion engine analyzes the user's emotional state and returns the results to the server.

[0825] Specific operation: The emotion engine analyzes the user's text input and behavioral data, generates emotion labels such as "satisfied" or "anxiety," and returns them to the server.

[0826] Step 13:

[0827] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[0828] Specific operation: An algorithm is applied that dynamically changes notification messages and suggestions depending on the user's emotional state.

[0829] Step 14:

[0830] The server formats the details of the identified business or individual and notifies the user.

[0831] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[0832] Step 15:

[0833] The terminal displays the data received from the server on the user's device, displaying detailed information about businesses and individuals willing to buy the items at the highest price.

[0834] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[0835] Step 16:

[0836] The user contacts businesses and individuals based on the information provided.

[0837] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[0838] Example 2

[0839] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0840] Currently, systems that allow users to sell items efficiently and at optimal prices require simple and fast procedures for data entry and searching for buyers. However, conventional systems have had difficulty automating these procedures and providing feedback that reflects the user's emotional state. Furthermore, they often lack functionality to enhance user satisfaction throughout the entire selling process.

[0841] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for a buyer or individual based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for recognizing the user's emotional state and adjusting the notification content and suggestions. This allows the user to efficiently sell items at the highest price, and furthermore, by receiving feedback based on emotional recognition, the user can achieve a better user experience and greater satisfaction.

[0842] "User" means an individual or corporation that wishes to sell items using the system.

[0843] "Detailed item information" refers to attribute information such as the name, brand, model number, and condition of the item being sold.

[0844] "Item image data" refers to a photo or image file of the item being sold.

[0845] A "generative AI model" is an algorithm or system that extracts specific patterns or features from data provided to it.

[0846] A "prompt sentence" is an input sentence that instructs an AI model for a specific purpose.

[0847] "Item features" refers to information about the appearance and condition of an item extracted through image analysis.

[0848] An "item profile" is a data set that integrates detailed information and feature information about an item.

[0849] A "buyer or individual" is a business or individual who is willing to buy items that a user wishes to sell.

[0850] An "emotion engine" is a software module for analyzing and assessing a user's emotional state.

[0851] The "means for adjusting notification content and suggestions" is a mechanism that has the function of dynamically changing notification content and suggestions based on the user's emotional state.

[0852] The present invention is a system that allows users to efficiently sell their items at the optimal price. The system automatically searches for buyers and individuals by allowing users to input detailed information about the items and upload image data, and identifies the person or seller offering the highest purchase price. The system also has a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[0853] Hardware and software used

[0854] Hardware

[0855] 1. Device: The smartphone, tablet, or computer used by the user.

[0856] 2. Server: A cloud server for data analysis, search, notifications, etc.

[0857] software

[0858] 1. Web form or mobile app: An interface for users to enter and upload item details and image data.

[0859] 2. Generative AI models: Used to extract features from image data of items. Specifically, Azure Computer Vision API and Google Cloud Vision API are examples.

[0860] 3. Emotion engine: Software for analyzing the user's emotional state, such as Amazon Rekognition or Microsoft Azure Emotion API.

[0861] 4. Database: A storage system for storing detailed item information, image data, extracted features, item profiles, buyer information, etc.

[0862] Data processing and calculation

[0863] Data Entry

[0864] Users access the system using a terminal, enter details of the item they want to sell (such as name, brand, model number, and condition) through a web form or mobile app, and take photos of the item using a smartphone or camera and upload them to the system.

[0865] Data Packaging and Transmission

[0866] The device packages the entered details and image data. This package contains text data in JSON format and Base64-encoded image data. This data is sent to the server via an HTTP POST request.

[0867] Data analysis

[0868] The server analyzes the received package. First, it parses the JSON data to separate it into text data and image data. Next, it creates a prompt to send to the generative AI model. An example of a specific prompt would be, "Please extract the features of the item shown in this image."

[0869] Item Profile Generation

[0870] The server receives the feature information returned by the generative AI model (e.g., "Sony α6000," "with optical zoom lens," "some scratches") and combines it with the detailed information entered by the user to generate an item profile, which is then stored in an SQL database.

[0871] Search and identify buyers and individuals

[0872] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual offering the highest purchase price. The search is performed using SQL or NoSQL queries.

[0873] Emotional state analysis

[0874] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavioral data, such as keystroke speed and microexpressions, to evaluate the user's current emotional state. Based on the evaluation results, the notification content and suggestions are adjusted. For example, the content may be adjusted to say, "Don't worry. We've found the best buy price for you."

[0875] Notification of results

[0876] The server formats and notifies the user of the details of the identified merchant or individual (such as name, contact information, and offered price). This data is sent to the user's device in HTML or JSON format. The device displays the data received from the server on the user's device, allowing the user to review the information and contact the merchant if necessary.

[0877] Specific examples

[0878] Case: Selling old digital cameras and emotion recognition

[0879] 1. A user opens the app to sell a Sony a6000.

[0880] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[0881] 3. The terminal sends the input information and image data to the server.

[0882] 4. The server sends the image data to a generative AI that extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[0883] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[0884] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[0885] 7. The server utilizes an emotion engine to assess the user's emotional state.

[0886] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[0887] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., "Congratulations, you found the best buy price!" if a happy emotion is recognized).

[0888] 10. The server formats the identified vendor information and notifies the user.

[0889] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[0890] As described above, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[0891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0892] Step 1: Receiving User Input

[0893] Users access the system using a web form or mobile app. They enter details of the item they want to sell (such as name, brand, model number, and condition), and they also take a photo of the item and upload the image data to the system.

[0894] Input: Item details, image data

[0895] Output: User input data (details and image data)

[0896] Step 2: Packaging and sending the data

[0897] The device packages the detailed information and image data entered by the user. This package contains text data in JSON format and Base64-encoded image data. The device sends this package to the server via an HTTP POST request.

[0898] Input: User-entered data (details and image data)

[0899] Output: HTTP POST request (details in JSON format and Base64 encoded image data)

[0900] Step 3: Receiving and analyzing data

[0901] The server analyzes the received HTTP POST request. It parses the JSON data to separate the text data from the image data. It then creates a prompt to analyze the image data. The specific prompt is "Please extract the characteristics of the item shown in this image."

[0902] Input: HTTP POST request (detailed information in JSON format and Base64 encoded image data)

[0903] Output: Text data, image data, prompt

[0904] Step 4: Analyzing the image data

[0905] The server sends the image data to a generative AI model, which extracts the item's features from the image using the Azure Computer Vision API or Google Cloud Vision API.

[0906] Input: Image data, prompt ("Please extract the features of the item in this image.")

[0907] Output: Characteristic information (e.g. "Sony α6000", "with optical zoom lens", "some scratches")

[0908] Step 5: Generate item profiles

[0909] The server combines the feature information returned by the generative AI model with the details entered by the user to generate an item profile, which is then stored in an SQL database.

[0910] Input: characteristic information, detailed information

[0911] Output: Item Profiles (records stored in a SQL database)

[0912] Step 6: Search the database of buyers and individuals

[0913] The server searches a database of buyers and individuals based on the generated item profile. This search uses SQL or NoSQL queries to identify the buyer or individual offering the highest purchase price.

[0914] Input: Item Profile

[0915] Output: List of buyers and individuals, the buyer or individual who offers the highest purchase price

[0916] Step 7: Analyze emotional state

[0917] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavior data and evaluates the user's current emotional state.

[0918] Input: User input data, behavioral data

[0919] Output: Emotion evaluation result (e.g., "Satisfied" or "Anxious")

[0920] Step 8: Adjust your notifications

[0921] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine. For example, if the user is rated as "anxious," the content will be adjusted to something like "Don't worry. We've found the best buy price for you."

[0922] Input: Emotion evaluation result

[0923] Output: Adjusted notification content

[0924] Step 9: Notification of results

[0925] The server formats and transmits the details of the identified merchant or individual (such as name, contact details, and offered price) to the user's terminal.

[0926] Input: Business or individual details, tailored notification content

[0927] Output: A message to inform the user

[0928] Step 10: Display to the User

[0929] The terminal displays the data received from the server on the user's device, allowing the user to contact a seller and sell the item at the highest price possible.

[0930] Input: A notification message to the user

[0931] Output: Displayed notification content

[0932] The above is the specific flow of program processing in the system of the present invention.

[0933] (Application example 2)

[0934] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] In conventional item selling systems, users had to spend a lot of time and effort to find the most suitable buyer. Furthermore, because notifications and suggestions were made uniformly without taking the user's emotional state into consideration, it was difficult to improve the user experience. Furthermore, when analyzing image data, it was difficult to extract features with high accuracy, and accurate evaluation of items was often impossible. This reduced the reliability and usability of the system.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0937] In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for potential buyers based on the generated item profile, means for identifying the potential buyer offering the highest desired purchase price from the search results, means for notifying the user of detailed information of the identified potential buyer, and means for recognizing the user's emotional state and adjusting the notification content and proposals based on that state. This allows the user to efficiently find the most suitable potential buyer, and the user experience is improved by responding in consideration of emotions.

[0938] A "user" is someone who uses this system to provide detailed information and image data about an item and find the most suitable buyer.

[0939] "Detailed item information" refers to specific information about the item being sold, such as the name, brand, model number, and condition.

[0940] "Image data" refers to photographs or images of the items being sold, which visually indicate the condition and characteristics of the items.

[0941] "Item characteristics" refers to the characteristics and attributes related to the appearance and condition of an item that are obtained by analyzing image data.

[0942] An "item profile" is a comprehensive description of an item that is generated by integrating user-supplied details with analyzed image data features.

[0943] "Purchaser" refers to a purchasing company or individual who wishes to purchase an item.

[0944] "Emotional state" refers to the emotional state that a user shows while using the system, and includes emotional expressions such as joy and anxiety.

[0945] "Notification content" refers to the method and message used to convey to the user detailed information about the most suitable buyer and the desired purchase price.

[0946] "Suggestions" refer to specific actions or information that are based on the user's emotional state and are intended to improve the user experience.

[0947] The present invention provides a system that allows users to sell their items efficiently at optimal prices. The system improves the user experience by recognizing the user's emotional state and adjusting notifications and suggestions accordingly. Specific embodiments of the present invention are described below.

[0948] System Configuration

[0949] This system mainly consists of the following components:

[0950] 1. User device: The device used by the user, such as a smartphone or computer.

[0951] 2. Server: A central system that processes information and connects to a database of potential buyers.

[0952] 3. Generative AI services: External services for analyzing image data, such as Google Cloud Vision API or IBM Watson Visual Recognition.

[0953] 4. Emotion recognition engine: Software for assessing the user's emotional state, such as the Affectiva SDK or Microsoft Azure Emotion API.

[0954] Program processing flow

[0955] Receiving User Input

[0956] The user inputs detailed information (such as name, brand, model number, and condition) of the item they wish to sell using a user terminal. They also take a picture of the item and upload it to the system.

[0957] Data reception and analysis

[0958] The user device sends the entered detailed information and image data to the server. The server analyzes the data and processes the text and image data. The image data is sent to the generation AI service, which extracts the item's features.

[0959] Generate an item profile

[0960] The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[0961] Searching for and identifying potential buyers

[0962] The server searches a database of potential buyers based on the item profile and identifies the potential buyer offering the highest desired purchase price.

[0963] Use of emotion engine

[0964] The server activates an emotion recognition engine to recognize the user's emotional state. It analyzes the user's input and behavior data and evaluates the emotional state.

[0965] Notification of results

[0966] The server notifies the user of the details of the identified potential buyers (such as their names, contact information, and asking prices). The content of the notification is adjusted based on the evaluation results from the emotion recognition engine.

[0967] Specific examples

[0968] For example, if a user wants to sell an Apple iPhone 12, the system works as follows:

[0969] 1. The user enters "iPhone 12, Brand: Apple, Model: A2172, Condition: Good" and uploads a photo of the item.

[0970] 2. The user terminal sends the input information and image data to the server.

[0971] 3. The server analyzes the image using the Google Cloud Vision API and extracts features such as "iPhone 12," "no scratches," and "good working condition."

[0972] 4. The server integrates the extracted features and detailed information to generate an item profile and stores it in a database.

[0973] 5. The server searches the database of potential buyers based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[0974] 6. The emotion engine assesses the user's emotional state and recognizes emotions such as joy and anxiety.

[0975] 7. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[0976] 8. The server formats the details of the identified buyers and notifies the user.

[0977] Prompt Sentence Examples

[0978] Please tell us the name of the item you are trying to sell.

[0979] "Please enter the item brand and model number"

[0980] "What is the current condition of the item (new, good condition, used, etc.)?"

[0981] Upload a photo of the item

[0982] In this way, users can efficiently find the best buyers, and emotional responses provide a better user experience.

[0983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0984] Step 1:

[0985] The user enters and uploads detailed information and image data for the item.

[0986] Using a user device (smartphone or PC), the user inputs detailed information about the item they want to sell, such as the name, brand, model number, and condition, and then takes or selects and uploads a photo of the item. Detailed information and image data are obtained as user input. This information is output as a temporary save on the device.

[0987] Step 2:

[0988] The device sends the input information and image data to the server.

[0989] The terminal packages the details and image data entered by the user and sends it to the server via an HTTP POST request. The input is the details and image data entered by the user, and the output is the transmission to the server.

[0990] Step 3:

[0991] The server receives and analyzes the data

[0992] The server receives data sent from the device and separates it into text data and image data. The image data is sent to an external AI generation service (Google Cloud Vision API or IBM Watson Visual Recognition) to extract its features. The input is data from the device, and the output is separated text data and image data with extracted features.

[0993] Step 4:

[0994] The server generates and saves the item profile.

[0995] The server integrates the extracted features with the text data to generate an item profile, which is then stored in a database. The input is the separated text data and extracted features, and the output is the item profile.

[0996] Step 5:

[0997] The server searches and identifies potential buyers

[0998] The server searches the database of potential buyers based on the item profile stored in the database. It identifies the potential buyer who offers the highest purchase price. The input is the item profile, and the output is the details of the most suitable potential buyer.

[0999] Step 6:

[1000] The server uses an emotion engine to evaluate the user's emotional state.

[1001] The server uses an emotion recognition engine (Affectiva SDK or Microsoft Azure Emotion API) to analyze the user's input and behavioral data and evaluate their emotional state. The input is the user's behavioral data, and the output is the evaluated user's emotional state.

[1002] Step 7:

[1003] The server adjusts the notification content and notifies the user.

[1004] The server adjusts the notification content and proposals based on the evaluation results and notifies the user of the details of the identified potential buyers. The notification content is adjusted based on the user's emotional state. The input is the details of the identified potential buyers and the evaluated emotional state, and the output is a notification message.

[1005] Step 8:

[1006] The device displays the notification from the server to the user.

[1007] The terminal displays the notification message received from the server to the user. The user can contact the potential buyer based on this information. The input is the notification message from the server, and the output is the display to the user.

[1008] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1009] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1010] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1011] [Third embodiment]

[1012] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1013] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1014] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1015] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1016] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1017] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1018] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1019] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1020] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1021] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1022] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1023] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1024] System Overview

[1025] The present invention is a system that allows users to sell their items efficiently at the best price. By inputting detailed information about the item and uploading image data, the system automatically searches for buyers and individuals and identifies the person or business offering the highest purchase price.

[1026] Program processing description

[1027] Step 1: Accepting user input

[1028] 1. User: Uses a web form or mobile app to enter details about the item they want to sell (e.g., name, brand, model number, condition, etc.).

[1029] 2. User: Takes a picture of the item and uploads it to the system.

[1030] Step 2: Receiving and analyzing data

[1031] 1. Terminal: Packages the entered details and image data and sends them to the server.

[1032] 2. Server: Analyzes the received data and separates text data from image data.

[1033] 3. Server: Sends image data to an external generative AI service to extract item features.

[1034] Step 3: Generate an item profile

[1035] 1. Server: Based on the extracted features, it integrates them with the input details to generate an item profile.

[1036] 2. Server: Stores the item profiles in a database.

[1037] Step 4: Search and identify buyers and individuals

[1038] 1. Server: Searches a database of buyers and individuals based on the item profile.

[1039] 2. Server: Identify the company or individual offering the highest purchase price from the search results.

[1040] Step 5: Notification of results

[1041] 1. Server: Formats and sends details of identified businesses and individuals (e.g., name, contact information, resale price, etc.) to the user.

[1042] 2. Device: The data received from the server is displayed on the user's device, allowing the user to review the information.

[1043] Specific examples

[1044] Case: Selling an old digital camera

[1045] 1. User: The user opens the app to sell a "Sony α6000."

[1046] 2. User: Enter the brand "Sony", model number "α6000", condition "good", and upload a photo of the camera.

[1047] 3. Terminal: Sends input information and image data to the server.

[1048] 4. Server: Sends image data to the generative AI, which extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1049] 5. Server: Integrates the extracted features with the input information to generate item profiles and stores them in a database.

[1050] 6. Server: Searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1051] 7. Server: Provides the merchant details to the user.

[1052] 8. Terminal: The information of the vendor received from the server is displayed on the terminal, and the user contacts the vendor based on that information.

[1053] In this way, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1057] What happens: The user enters data into form fields and presses the "Submit" button.

[1058] Step 2:

[1059] Users take photos of items and upload them to the system.

[1060] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[1061] Step 3:

[1062] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1063] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[1064] Step 4:

[1065] The server analyzes the received request and separates the text data from the image data.

[1066] What happens: The server parses the request body and stores the text and image fields separately.

[1067] Step 5:

[1068] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[1069] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[1070] Step 6:

[1071] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[1072] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[1073] Step 7:

[1074] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[1075] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[1076] Step 8:

[1077] The server stores the generated item profile in a database.

[1078] Specific behavior: Inserts the item profile into the appropriate table in the database.

[1079] Step 9:

[1080] The server generates a query to search for buyers or individuals based on the item profile.

[1081] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[1082] Step 10:

[1083] The server identifies the trader or individual offering the highest purchase price from the search results.

[1084] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[1085] Step 11:

[1086] The server formats the details of the identified business or individual and notifies the user.

[1087] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[1088] Step 12:

[1089] The terminal receives the response on the user's terminal and displays detailed information about the trader or individual who is willing to buy the item at the highest price.

[1090] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[1091] Step 13:

[1092] The user proceeds with the transaction using the provided merchant or personal contact information.

[1093] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[1094] Example 1

[1095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1096] Conventional systems make it difficult for users to sell items efficiently at the best price. Specifically, users must contact multiple buyers and individuals and compare prices, which takes time and effort, and often results in the user being unable to find the best price. Therefore, there is a need for a system that allows users to easily and quickly find the buyer or individual who offers the highest purchase price.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1098] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for packaging the input detailed information and image data and transmitting the packaged data to the server, means for separating the received text data from the image data, means for transmitting the image data to an external generative AI service and extracting item features, means for integrating the extracted features and detailed information to generate an item profile, means for storing the generated item profile in a database, means for searching for buyers or individuals based on the item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, and means for notifying the user of detailed information about the identified buyer or individual. This allows users to easily and quickly find the buyer or individual offering the highest purchase price.

[1099] "User" means an individual or corporation that wishes to sell items using the system.

[1100] "Detailed item information" refers to various information entered by the user, such as the item's name, brand, model number, and condition.

[1101] "Item image data" refers to a photo of an item that a user takes and uploads to the system.

[1102] "Packaging" is the process of combining detailed information and image data of an item into a single data format (e.g., JSON format).

[1103] "Text data" is the text portion of the detailed information about an item entered by the user.

[1104] "Image data" refers to the image file uploaded as a photo of the item.

[1105] A "generative AI service" is an external artificial intelligence service that performs image analysis, etc.

[1106] "Feature extraction" means deriving information such as the attributes and condition of an item from image data.

[1107] An "item profile" is data that integrates detailed information about an item with extracted features.

[1108] A "database" is a data storage system that stores and manages item profiles, user information, etc.

[1109] A "buyer or individual" is a business or individual willing to purchase an item.

[1110] "Search" is the process of locating buyers or individuals within the database based on an item profile.

[1111] The "purchase price" is the purchase price offered by a buyer or individual for the item.

[1112] "Notification" is the act of notifying a user of specified information.

[1113] The present invention is a system that allows users to sell their items efficiently at the optimal price. This system extracts item characteristics based on detailed information and image data provided by the user, and generates an item profile. It then automatically searches for buyers and individuals based on the item profile, identifies the buyer or individual who offers the highest purchase price, and notifies the user of that information.

[1114] Hardware and software used

[1115] User device: A device such as a smartphone or PC where a user inputs information. This device is built using web forms using HTML, CSS, and JavaScript, or a mobile app using React Native.

[1116] Server: The central system that receives input information, analyzes data, and communicates with generative AI services. Scripts are executed using Python or Node.js.

[1117] Data storage system: Databases such as Amazon RDS and MongoDB are used to store information.

[1118] Generative AI services: Google Cloud Vision API and other services are used to analyze image data.

[1119] Program processing

[1120] 1. Using a web form or mobile app, users enter detailed information about the item they want to sell (such as name, brand, model number, and condition), and also take a photo of the item with a smartphone or digital camera and upload it to the system as image data.

[1121] 2. The device packages the input text data and image data in JSON format and sends it to the server via an HTTP POST request.

[1122] 3. The server receives the received data using the API gateway and separates the text data from the image data using a data parser.

[1123] 4. The server sends the image data to a generative AI service, which extracts item features (e.g., label information, tags, metadata) from the image data.

[1124] 5. The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[1125] 6. The server searches a database of buyers and individuals based on the item profile, and uses a sorting algorithm to identify the buyer or individual offering the highest price.

[1126] 7. The server compiles detailed information about the identified businesses and individuals (e.g., name, contact information, purchase price) into a JSON response and sends it to the user's device.

[1127] 8. The terminal parses the received response data and displays it visually on the user's device, allowing the user to contact the seller and sell the item.

[1128] Examples and prompts

[1129] Case: Selling an old digital camera "Sony α6000"

[1130] 1. The user opens the smartphone app, enters the brand "Sony," the model number "α6000," and the condition "good," then takes a photo with the camera and uploads it.

[1131] 2. The device compiles the input text information and image data in JSON format and sends it to the server using an HTTP POST request.

[1132] 3. The server analyzes the received data and sends the image data to a generative AI service, which extracts features such as "Sony α6000," "with optical zoom lens," and "some scratches."

[1133] 4. The server integrates the extracted features with the input information to generate an item profile and save it in the database.

[1134] 5. The server searches a database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1135] 6. The server notifies the user of the details of the identified vendor.

[1136] 7. The terminal displays the received information about the supplier, and the user contacts the supplier based on that information.

[1137] Prompt Sentence Examples

[1138] Upload an image of your Sony α6000 and enter details such as its condition and model number. Our system will search for the best buyers and notify you of the highest bidder.

[1139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1140] Step 1:

[1141] Users launch a web form or mobile app, enter details about the item they want to sell (such as name, brand, model number, and condition), and take and upload a photo of the item.

[1142] Input: Item text information, item image data

[1143] How it works: Using a smartphone or digital camera, the user enters text information, takes a photo, and uploads the image data.

[1144] Output: Input and image upload completion status on the user's device

[1145] Step 2:

[1146] The device packages the entered text information and image data in JSON format and sends it to the server via an HTTP POST request.

[1147] Input: User-entered text information, item image data

[1148] How it works: Using JavaScript and React Native, the input form data and image files are converted into JSON format and an HTTP POST request is created.

[1149] Output: Request data in JSON format, status of sending to server

[1150] Step 3:

[1151] The server receives the received data using an API gateway and begins separating and analyzing it, parsing and separating text data and image data.

[1152] Input: Request data in JSON format

[1153] How it works: Using Python and Node.js scripts, JSON data is parsed and separated into text data and image data.

[1154] Output: Separated text data, separated image data

[1155] Step 4:

[1156] The server sends the image data to a generative AI service, which extracts the item's features.

[1157] Input: Separated image data

[1158] How it works: Sends an API request to a generative AI service (e.g., Google Cloud Vision API) to perform feature extraction processing.

[1159] Output: Feature data from generative AI services (e.g., label information, tags, metadata)

[1160] Step 5:

[1161] The server integrates the extracted features with the input text information to generate an item profile.

[1162] Input: separated text data, feature data from generative AI services

[1163] Operation: Using a data structure, text data and feature data are integrated and organized into item profiles.

[1164] Output: Item profile (integrated data)

[1165] Step 6:

[1166] The server stores the generated item profile in a database.

[1167] Input: Item Profile

[1168] How it works: Inserts item profiles into a database (e.g., Amazon RDS, MongoDB) using a SQL or NoSQL query.

[1169] Output: Item profile saved in database, save status

[1170] Step 7:

[1171] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual who offers the highest desired purchase price.

[1172] Input: Item Profile

[1173] How it works: It uses SQL or NoSQL query languages ​​to search for businesses and individuals in a database, then uses a sorting algorithm to identify those offering the best buyback price.

[1174] Output: Details of the businesses and individuals offering the highest purchase prices

[1175] Step 8:

[1176] The server creates a response to notify the user of detailed information about the identified business or individual, and sends it to the terminal.

[1177] Input: Details of the company or individual offering the highest purchase price

[1178] Operation: Creates response data in JSON format and sends it to the user's device via an HTTP POST or GET request.

[1179] Output: JSON format response data, sending status

[1180] Step 9:

[1181] The terminal parses the response data received from the server and displays it visually on the user's device, allowing the user to contact the merchant based on that information.

[1182] Input: JSON format response data from the server

[1183] What it does: Parses the response data and displays it in a user interface using React Native or another UI framework, visually rendering the information for the user to see.

[1184] Output: details of the business or individual displayed to the user, and the user's actions (e.g., contact)

[1185] (Application example 1)

[1186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1187] Currently, in order to sell their items at a high price, users must compare multiple buyers, which takes time and effort. It is also difficult to determine whether the purchase price is fair. In particular, extracting item features using image data requires specialized knowledge and high-performance analytical technology, which is difficult for average users. This can result in users missing out on opportunities to sell their items at the optimal price. There is a need to solve these problems.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1189] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for generating an item profile by integrating the extracted features and detailed information, means for searching for buyers or individuals based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for the user to input detailed item information and image data using a mobile device, thereby enabling the user to easily sell items at the optimal price.

[1190] A "User" is any person or entity that seeks to sell an item using the System.

[1191] "Detailed item information" refers to information such as the name, brand, model number, and condition of the item being sold.

[1192] "Item image data" refers to the photograph or image file data of the item you are selling.

[1193] "Features of an item" are specific attributes and characteristics of an item extracted by analyzing image data.

[1194] An "item profile" is a data set that integrates detailed information and characteristics of an item.

[1195] A "buyer or individual" is a business or individual willing to purchase an item from a user.

[1196] A "mobile device" is a portable information processing device such as a smartphone or tablet.

[1197] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze image data and extract features.

[1198] "Searching" is the process of searching through data in a database to find information that meets specific criteria.

[1199] A "notification" is a message or alert sent to inform a user of specific information.

[1200] System Overview

[1201] The basic function of the invention is a system that helps users sell their items efficiently at the best price. Users input detailed information about the item and upload image data, and the system automatically searches for buyers and individuals and identifies the one offering the highest purchase price.

[1202] Hardware and software used

[1203] Hardware: Smartphone (iOS or Android), server

[1204] Software: Python, Flask (web framework), PIL (image processing library), generative AI model API

[1205] 1. Receiving data

[1206] Using a smartphone app, users input detailed information about the items they want to sell (e.g., name, brand, model number, condition, etc.) and also take and upload photos of the items to provide image data to the system.

[1207] 2. Data Analysis

[1208] The device sends the input details and image data to a server, which then sends the image data to a generative AI model to extract specific features. This generative AI model then uses machine learning and deep learning to analyze the image.

[1209] 3. Generate Item Profiles

[1210] The server combines the extracted features and detailed information to generate an item profile, which is then stored in a database.

[1211] 4. Search for buyers and individuals

[1212] Based on the item profile, the server searches a database of buyers and individuals, and from the search results, identifies the buyer or individual offering the highest purchase price.

[1213] 5. Notification of results

[1214] The system formats and notifies the user of the details of the identified businesses and individuals, allowing the user to check the best buyback price information on the smartphone app and contact the businesses or individuals.

[1215] Example

[1216] For example, if a user wants to sell their old digital camera, a Sony α6000, they can use the system as follows:

[1217] 1. The user opens the smartphone app and enters the camera's details (brand "Sony," model number "α6000," condition "good").

[1218] 2. Take a photo with the camera and upload the image data through the app.

[1219] 3. The app sends the input information and image data to the server.

[1220] 4. The server sends the image data to a generative AI service to extract the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1221] 5. The extracted features are integrated with the input information to generate an item profile, which is then stored in the database.

[1222] 6. Based on the item profile, a database of buyers and individuals is searched to identify the buyer offering the highest purchase price.

[1223] 7. The details of the best fit supplier will be sent to the user's smartphone app.

[1224] Prompt Sentence Examples

[1225] An example of a prompt to input to a generative AI model is as follows:

[1226] "Extract item characteristics from the provided image file. These characteristics should include the item's name, brand, model number, condition, and any special notes."

[1227] This allows users to easily sell their digital cameras and other items through the system for the highest possible price.

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1:

[1230] Using a smartphone app, users enter detailed information about the item they want to sell (e.g., name, brand, model number, condition) and upload image data of the item. Specifically, they enter the required text data (name, brand, model number, condition) into the app's form, take a photo or select an image from the gallery, and press the upload button. The entered detailed information and image data are temporarily stored in the app's local storage and then sent to the server for subsequent processing.

[1231] Step 2:

[1232] The device packages the details entered by the user and the uploaded image data and sends them to the server. The details are sent in JSON format, and the image data is sent in binary format. The server receives this data and prepares it for the next analysis process.

[1233] Step 3:

[1234] The server sends the received image data to the generative AI model API to extract the item's features. The image data is passed to the generative AI model using the prompt, "Please extract the item's features from the specified image file. The features should include the item's name, brand, model number, condition, and any special notes." During this process, the generative AI model uses machine learning and deep learning algorithms to analyze the image and return the extracted feature information (e.g., name "Sony α6000," condition "good," special notes "with optical zoom lens, some scratches").

[1235] Step 4:

[1236] The server combines the extracted feature information with the detailed information entered by the user to generate an item profile. Specifically, the feature information and detailed information are merged into a single JSON object, which is then saved in the database as the item profile. This provides a clear overall picture of the item.

[1237] Step 5:

[1238] The server searches for buyers or individuals based on the generated item profile. The server queries the buyer or individual database using the item's category and characteristics as keys to obtain a list of candidates. The server also obtains the desired purchase price for each candidate.

[1239] Step 6:

[1240] The server identifies the company or individual who offers the highest purchase price from the list of candidates. The server compares the purchase prices and extracts detailed information (e.g., name, contact information, purchase price) of the company or individual who offered the highest price. This determines the most advantageous seller for the user.

[1241] Step 7:

[1242] The server formats and sends detailed information about the identified sellers or individuals to the user's device, including the seller's or individual's name, contact information, and purchase price, allowing the user to identify the most suitable seller or individual within the app and contact them based on that information.

[1243] Through the above processing steps, users can easily sell items at the best price.

[1244] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1245] System Overview

[1246] The present invention is a system that allows users to efficiently sell their items at the optimal price. By entering detailed information about the item and uploading image data, users can use the system to automatically search for buyers and individuals and identify the person or business offering the highest purchase price. Furthermore, the present invention adds a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[1247] Program processing description

[1248] Step 1: Accepting user input

[1249] 1. A user uses a web form or mobile app to enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1250] 2. The user takes a photo of the item and uploads it to the system.

[1251] Step 2: Receiving and analyzing data

[1252] 1. The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1253] 2. The server analyzes the received request and separates the text data from the image data.

[1254] 3. The server sends the image data to an external generative AI service to extract the item's features.

[1255] Step 3: Generate an item profile

[1256] 1. The server generates an item profile based on the extracted features and integrates them with the input details.

[1257] 2. The server stores the item profile in a database.

[1258] Step 4: Search and identify buyers and individuals

[1259] 1. The server searches a database of buyers and individuals based on the item profile.

[1260] 2. The server identifies the business or individual offering the highest purchase price from the search results.

[1261] Step 5: Use the Emotion Engine

[1262] 1. The server launches the emotion engine to recognize the user's emotional state.

[1263] 2. The emotion engine analyzes user input and behavioral data to assess the user's current emotional state.

[1264] 3. The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[1265] Step 6: Notification of results

[1266] 1. The server formats the details of the identified merchant or individual (such as name, contact information, and offered price) and notifies the user.

[1267] 2. The terminal displays the data received from the server on the user's device so that the user can check the information.

[1268] Specific examples

[1269] Case: Selling old digital cameras and emotion recognition

[1270] 1. A user opens the app to sell a Sony a6000.

[1271] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[1272] 3. The terminal sends the input information and image data to the server.

[1273] 4. The server sends the image data to a generative AI that extracts the item's characteristics (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1274] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[1275] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[1276] 7. The server utilizes an emotion engine to assess the user's emotional state.

[1277] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[1278] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[1279] 10. The server formats the identified vendor information and notifies the user.

[1280] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[1281] In this way, by using the system based on the present invention, users can easily find businesses or individuals that will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[1282] The processing flow will be explained below.

[1283] Step 1:

[1284] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1285] What happens: The user enters data into form fields and presses the "Submit" button.

[1286] Step 2:

[1287] Users take photos of items and upload them to the system.

[1288] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[1289] Step 3:

[1290] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1291] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[1292] Step 4:

[1293] The server analyzes the received request and separates the text data from the image data.

[1294] What happens: The server parses the request body and stores the text and image fields separately.

[1295] Step 5:

[1296] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[1297] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[1298] Step 6:

[1299] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[1300] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[1301] Step 7:

[1302] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[1303] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[1304] Step 8:

[1305] The server stores the generated item profile in a database.

[1306] Specific behavior: Inserts the item profile into the appropriate table in the database.

[1307] Step 9:

[1308] The server generates a query to search for buyers or individuals based on the item profile.

[1309] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[1310] Step 10:

[1311] The server identifies the trader or individual offering the highest purchase price from the search results.

[1312] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[1313] Step 11:

[1314] The server activates an emotion engine to recognize the user's emotional state.

[1315] Specific operation: Sends user input data and behavioral data to the emotion engine and requests emotion analysis.

[1316] Step 12:

[1317] The emotion engine analyzes the user's emotional state and returns the results to the server.

[1318] Specific operation: The emotion engine analyzes the user's text input and behavioral data, generates emotion labels such as "satisfied" or "anxiety," and returns them to the server.

[1319] Step 13:

[1320] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[1321] Specific operation: An algorithm is applied that dynamically changes notification messages and suggestions depending on the user's emotional state.

[1322] Step 14:

[1323] The server formats the details of the identified business or individual and notifies the user.

[1324] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[1325] Step 15:

[1326] The terminal displays the data received from the server on the user's device, displaying detailed information about businesses and individuals willing to buy the items at the highest price.

[1327] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[1328] Step 16:

[1329] The user contacts businesses and individuals based on the information provided.

[1330] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[1331] Example 2

[1332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Currently, systems that allow users to sell items efficiently and at optimal prices require simple and fast procedures for data entry and searching for buyers. However, conventional systems have had difficulty automating these procedures and providing feedback that reflects the user's emotional state. Furthermore, they often lack functionality to enhance user satisfaction throughout the entire selling process.

[1334] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for a buyer or individual based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for recognizing the user's emotional state and adjusting the notification content and suggestions. This allows the user to efficiently sell items at the highest price, and furthermore, by receiving feedback based on emotional recognition, the user can achieve a better user experience and greater satisfaction.

[1335] "User" means an individual or corporation that wishes to sell items using the system.

[1336] "Detailed item information" refers to attribute information such as the name, brand, model number, and condition of the item being sold.

[1337] "Item image data" refers to a photo or image file of the item being sold.

[1338] A "generative AI model" is an algorithm or system that extracts specific patterns or features from data provided to it.

[1339] A "prompt sentence" is an input sentence that instructs an AI model for a specific purpose.

[1340] "Item features" refers to information about the appearance and condition of an item extracted through image analysis.

[1341] An "item profile" is a data set that integrates detailed information and feature information about an item.

[1342] A "buyer or individual" is a business or individual who is willing to buy items that a user wishes to sell.

[1343] An "emotion engine" is a software module for analyzing and assessing a user's emotional state.

[1344] The "means for adjusting notification content and suggestions" is a mechanism that has the function of dynamically changing notification content and suggestions based on the user's emotional state.

[1345] The present invention is a system that allows users to efficiently sell their items at the optimal price. The system automatically searches for buyers and individuals by allowing users to input detailed information about the items and upload image data, and identifies the person or seller offering the highest purchase price. The system also has a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[1346] Hardware and software used

[1347] Hardware

[1348] 1. Device: The smartphone, tablet, or computer used by the user.

[1349] 2. Server: A cloud server for data analysis, search, notifications, etc.

[1350] software

[1351] 1. Web form or mobile app: An interface for users to enter and upload item details and image data.

[1352] 2. Generative AI models: Used to extract features from image data of items. Specifically, Azure Computer Vision API and Google Cloud Vision API are examples.

[1353] 3. Emotion engine: Software for analyzing the user's emotional state, such as Amazon Rekognition or Microsoft Azure Emotion API.

[1354] 4. Database: A storage system for storing detailed item information, image data, extracted features, item profiles, buyer information, etc.

[1355] Data processing and calculation

[1356] Data Entry

[1357] Users access the system using a terminal, enter details of the item they want to sell (such as name, brand, model number, and condition) through a web form or mobile app, and take photos of the item using a smartphone or camera and upload them to the system.

[1358] Data Packaging and Transmission

[1359] The device packages the entered details and image data. This package contains text data in JSON format and Base64-encoded image data. This data is sent to the server via an HTTP POST request.

[1360] Data analysis

[1361] The server analyzes the received package. First, it parses the JSON data to separate it into text data and image data. Next, it creates a prompt to send to the generative AI model. An example of a specific prompt would be, "Please extract the features of the item shown in this image."

[1362] Item Profile Generation

[1363] The server receives the feature information returned by the generative AI model (e.g., "Sony α6000," "with optical zoom lens," "some scratches") and combines it with the detailed information entered by the user to generate an item profile, which is then stored in an SQL database.

[1364] Search and identify buyers and individuals

[1365] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual offering the highest purchase price. The search is performed using SQL or NoSQL queries.

[1366] Emotional state analysis

[1367] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavioral data, such as keystroke speed and microexpressions, to evaluate the user's current emotional state. Based on the evaluation results, the notification content and suggestions are adjusted. For example, the content may be adjusted to say, "Don't worry. We've found the best buy price for you."

[1368] Notification of results

[1369] The server formats and notifies the user of the details of the identified merchant or individual (such as name, contact information, and offered price). This data is sent to the user's device in HTML or JSON format. The device displays the data received from the server on the user's device, allowing the user to review the information and contact the merchant if necessary.

[1370] Specific examples

[1371] Case: Selling old digital cameras and emotion recognition

[1372] 1. A user opens the app to sell a Sony a6000.

[1373] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[1374] 3. The terminal sends the input information and image data to the server.

[1375] 4. The server sends the image data to a generative AI that extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1376] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[1377] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[1378] 7. The server utilizes an emotion engine to assess the user's emotional state.

[1379] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[1380] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., "Congratulations, you found the best buy price!" if a happy emotion is recognized).

[1381] 10. The server formats the identified vendor information and notifies the user.

[1382] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[1383] As described above, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[1384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1385] Step 1: Receiving User Input

[1386] Users access the system using a web form or mobile app. They enter details of the item they want to sell (such as name, brand, model number, and condition), and they also take a photo of the item and upload the image data to the system.

[1387] Input: Item details, image data

[1388] Output: User input data (details and image data)

[1389] Step 2: Packaging and sending the data

[1390] The device packages the detailed information and image data entered by the user. This package contains text data in JSON format and Base64-encoded image data. The device sends this package to the server via an HTTP POST request.

[1391] Input: User-entered data (details and image data)

[1392] Output: HTTP POST request (details in JSON format and Base64 encoded image data)

[1393] Step 3: Receiving and analyzing data

[1394] The server analyzes the received HTTP POST request. It parses the JSON data to separate the text data from the image data. It then creates a prompt to analyze the image data. The specific prompt is "Please extract the characteristics of the item shown in this image."

[1395] Input: HTTP POST request (detailed information in JSON format and Base64 encoded image data)

[1396] Output: Text data, image data, prompt

[1397] Step 4: Analyzing the image data

[1398] The server sends the image data to a generative AI model, which extracts the item's features from the image using the Azure Computer Vision API or Google Cloud Vision API.

[1399] Input: Image data, prompt ("Please extract the features of the item in this image.")

[1400] Output: Characteristic information (e.g. "Sony α6000", "with optical zoom lens", "some scratches")

[1401] Step 5: Generate item profiles

[1402] The server combines the feature information returned by the generative AI model with the details entered by the user to generate an item profile, which is then stored in an SQL database.

[1403] Input: characteristic information, detailed information

[1404] Output: Item Profiles (records stored in a SQL database)

[1405] Step 6: Search the database of buyers and individuals

[1406] The server searches a database of buyers and individuals based on the generated item profile. This search uses SQL or NoSQL queries to identify the buyer or individual offering the highest purchase price.

[1407] Input: Item Profile

[1408] Output: List of buyers and individuals, the buyer or individual who offers the highest purchase price

[1409] Step 7: Analyze emotional state

[1410] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavior data and evaluates the user's current emotional state.

[1411] Input: User input data, behavioral data

[1412] Output: Emotion evaluation result (e.g., "Satisfied" or "Anxious")

[1413] Step 8: Adjust your notifications

[1414] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine. For example, if the user is rated as "anxious," the content will be adjusted to something like "Don't worry. We've found the best buy price for you."

[1415] Input: Emotion evaluation result

[1416] Output: Adjusted notification content

[1417] Step 9: Notification of results

[1418] The server formats and transmits the details of the identified merchant or individual (such as name, contact details, and offered price) to the user's terminal.

[1419] Input: Business or individual details, tailored notification content

[1420] Output: A message to inform the user

[1421] Step 10: Display to the User

[1422] The terminal displays the data received from the server on the user's device, allowing the user to contact a seller and sell the item at the highest price possible.

[1423] Input: A notification message to the user

[1424] Output: Displayed notification content

[1425] The above is the specific flow of program processing in the system of the present invention.

[1426] (Application example 2)

[1427] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1428] In conventional item selling systems, users had to spend a lot of time and effort to find the most suitable buyer. Furthermore, because notifications and suggestions were made uniformly without taking the user's emotional state into consideration, it was difficult to improve the user experience. Furthermore, when analyzing image data, it was difficult to extract features with high accuracy, and accurate evaluation of items was often impossible. This reduced the reliability and usability of the system.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1430] In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for potential buyers based on the generated item profile, means for identifying the potential buyer offering the highest desired purchase price from the search results, means for notifying the user of detailed information of the identified potential buyer, and means for recognizing the user's emotional state and adjusting the notification content and proposals based on that state. This allows the user to efficiently find the most suitable potential buyer, and the user experience is improved by responding in consideration of emotions.

[1431] A "user" is someone who uses this system to provide detailed information and image data about an item and find the most suitable buyer.

[1432] "Detailed item information" refers to specific information about the item being sold, such as the name, brand, model number, and condition.

[1433] "Image data" refers to photographs or images of the items being sold, which visually indicate the condition and characteristics of the items.

[1434] "Item characteristics" refers to the characteristics and attributes related to the appearance and condition of an item that are obtained by analyzing image data.

[1435] An "item profile" is a comprehensive description of an item that is generated by integrating user-supplied details with analyzed image data features.

[1436] "Purchaser" refers to a purchasing company or individual who wishes to purchase an item.

[1437] "Emotional state" refers to the emotional state that a user shows while using the system, and includes emotional expressions such as joy and anxiety.

[1438] "Notification content" refers to the method and message used to convey to the user detailed information about the most suitable buyer and the desired purchase price.

[1439] "Suggestions" refer to specific actions or information that are based on the user's emotional state and are intended to improve the user experience.

[1440] The present invention provides a system that allows users to sell their items efficiently at optimal prices. The system improves the user experience by recognizing the user's emotional state and adjusting notifications and suggestions accordingly. Specific embodiments of the present invention are described below.

[1441] System Configuration

[1442] This system mainly consists of the following components:

[1443] 1. User device: The device used by the user, such as a smartphone or computer.

[1444] 2. Server: A central system that processes information and connects to a database of potential buyers.

[1445] 3. Generative AI services: External services for analyzing image data, such as Google Cloud Vision API or IBM Watson Visual Recognition.

[1446] 4. Emotion recognition engine: Software for assessing the user's emotional state, such as the Affectiva SDK or Microsoft Azure Emotion API.

[1447] Program processing flow

[1448] Receiving User Input

[1449] The user inputs detailed information (such as name, brand, model number, and condition) of the item they wish to sell using a user terminal. They also take a picture of the item and upload it to the system.

[1450] Data reception and analysis

[1451] The user device sends the entered detailed information and image data to the server. The server analyzes the data and processes the text and image data. The image data is sent to the generation AI service, which extracts the item's features.

[1452] Generate an item profile

[1453] The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[1454] Searching for and identifying potential buyers

[1455] The server searches a database of potential buyers based on the item profile and identifies the potential buyer offering the highest desired purchase price.

[1456] Use of emotion engine

[1457] The server activates an emotion recognition engine to recognize the user's emotional state. It analyzes the user's input and behavior data and evaluates the emotional state.

[1458] Notification of results

[1459] The server notifies the user of the details of the identified potential buyers (such as their names, contact information, and asking prices). The content of the notification is adjusted based on the evaluation results from the emotion recognition engine.

[1460] Specific examples

[1461] For example, if a user wants to sell an Apple iPhone 12, the system works as follows:

[1462] 1. The user enters "iPhone 12, Brand: Apple, Model: A2172, Condition: Good" and uploads a photo of the item.

[1463] 2. The user terminal sends the input information and image data to the server.

[1464] 3. The server analyzes the image using the Google Cloud Vision API and extracts features such as "iPhone 12," "no scratches," and "good working condition."

[1465] 4. The server integrates the extracted features and detailed information to generate an item profile and stores it in a database.

[1466] 5. The server searches the database of potential buyers based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1467] 6. The emotion engine assesses the user's emotional state and recognizes emotions such as joy and anxiety.

[1468] 7. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[1469] 8. The server formats the details of the identified buyers and notifies the user.

[1470] Prompt Sentence Examples

[1471] Please tell us the name of the item you are trying to sell.

[1472] "Please enter the item brand and model number"

[1473] "What is the current condition of the item (new, good condition, used, etc.)?"

[1474] Upload a photo of the item

[1475] In this way, users can efficiently find the best buyers, and emotional responses provide a better user experience.

[1476] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1477] Step 1:

[1478] The user enters and uploads detailed information and image data for the item.

[1479] Using a user device (smartphone or PC), the user inputs detailed information about the item they want to sell, such as the name, brand, model number, and condition, and then takes or selects and uploads a photo of the item. Detailed information and image data are obtained as user input. This information is output as a temporary save on the device.

[1480] Step 2:

[1481] The device sends the input information and image data to the server.

[1482] The terminal packages the details and image data entered by the user and sends it to the server via an HTTP POST request. The input is the details and image data entered by the user, and the output is the transmission to the server.

[1483] Step 3:

[1484] The server receives and analyzes the data

[1485] The server receives data sent from the device and separates it into text data and image data. The image data is sent to an external AI generation service (Google Cloud Vision API or IBM Watson Visual Recognition) to extract its features. The input is data from the device, and the output is separated text data and image data with extracted features.

[1486] Step 4:

[1487] The server generates and saves the item profile.

[1488] The server integrates the extracted features with the text data to generate an item profile, which is then stored in a database. The input is the separated text data and extracted features, and the output is the item profile.

[1489] Step 5:

[1490] The server searches and identifies potential buyers

[1491] The server searches the database of potential buyers based on the item profile stored in the database. It identifies the potential buyer who offers the highest purchase price. The input is the item profile, and the output is the details of the most suitable potential buyer.

[1492] Step 6:

[1493] The server uses an emotion engine to evaluate the user's emotional state.

[1494] The server uses an emotion recognition engine (Affectiva SDK or Microsoft Azure Emotion API) to analyze the user's input and behavioral data and evaluate their emotional state. The input is the user's behavioral data, and the output is the evaluated user's emotional state.

[1495] Step 7:

[1496] The server adjusts the notification content and notifies the user.

[1497] The server adjusts the notification content and proposals based on the evaluation results and notifies the user of the details of the identified potential buyers. The notification content is adjusted based on the user's emotional state. The input is the details of the identified potential buyers and the evaluated emotional state, and the output is a notification message.

[1498] Step 8:

[1499] The device displays the notification from the server to the user.

[1500] The terminal displays the notification message received from the server to the user. The user can contact the potential buyer based on this information. The input is the notification message from the server, and the output is the display to the user.

[1501] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1502] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1503] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1504] [Fourth embodiment]

[1505] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1506] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1507] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1508] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1509] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1510] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1511] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1512] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1513] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1514] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1515] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1516] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1517] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1518] System Overview

[1519] The present invention is a system that allows users to sell their items efficiently at the best price. By inputting detailed information about the item and uploading image data, the system automatically searches for buyers and individuals and identifies the person or business offering the highest purchase price.

[1520] Program processing description

[1521] Step 1: Accepting user input

[1522] 1. User: Uses a web form or mobile app to enter details about the item they want to sell (e.g., name, brand, model number, condition, etc.).

[1523] 2. User: Takes a picture of the item and uploads it to the system.

[1524] Step 2: Receiving and analyzing data

[1525] 1. Terminal: Packages the entered details and image data and sends them to the server.

[1526] 2. Server: Analyzes the received data and separates text data from image data.

[1527] 3. Server: Sends image data to an external generative AI service to extract item features.

[1528] Step 3: Generate an item profile

[1529] 1. Server: Based on the extracted features, it integrates them with the input details to generate an item profile.

[1530] 2. Server: Stores the item profiles in a database.

[1531] Step 4: Search and identify buyers and individuals

[1532] 1. Server: Searches a database of buyers and individuals based on the item profile.

[1533] 2. Server: Identify the company or individual offering the highest purchase price from the search results.

[1534] Step 5: Notification of results

[1535] 1. Server: Formats and sends details of identified businesses and individuals (e.g., name, contact information, resale price, etc.) to the user.

[1536] 2. Device: The data received from the server is displayed on the user's device, allowing the user to review the information.

[1537] Specific examples

[1538] Case: Selling an old digital camera

[1539] 1. User: The user opens the app to sell a "Sony α6000."

[1540] 2. User: Enter the brand "Sony", model number "α6000", condition "good", and upload a photo of the camera.

[1541] 3. Terminal: Sends input information and image data to the server.

[1542] 4. Server: Sends image data to the generative AI, which extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1543] 5. Server: Integrates the extracted features with the input information to generate item profiles and stores them in a database.

[1544] 6. Server: Searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1545] 7. Server: Provides the merchant details to the user.

[1546] 8. Terminal: The information of the vendor received from the server is displayed on the terminal, and the user contacts the vendor based on that information.

[1547] In this way, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price.

[1548] The processing flow will be explained below.

[1549] Step 1:

[1550] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1551] What happens: The user enters data into form fields and presses the "Submit" button.

[1552] Step 2:

[1553] Users take photos of items and upload them to the system.

[1554] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[1555] Step 3:

[1556] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1557] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[1558] Step 4:

[1559] The server analyzes the received request and separates the text data from the image data.

[1560] What happens: The server parses the request body and stores the text and image fields separately.

[1561] Step 5:

[1562] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[1563] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[1564] Step 6:

[1565] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[1566] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[1567] Step 7:

[1568] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[1569] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[1570] Step 8:

[1571] The server stores the generated item profile in a database.

[1572] Specific behavior: Inserts the item profile into the appropriate table in the database.

[1573] Step 9:

[1574] The server generates a query to search for buyers or individuals based on the item profile.

[1575] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[1576] Step 10:

[1577] The server identifies the trader or individual offering the highest purchase price from the search results.

[1578] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[1579] Step 11:

[1580] The server formats the details of the identified business or individual and notifies the user.

[1581] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[1582] Step 12:

[1583] The terminal receives the response on the user's terminal and displays detailed information about the trader or individual who is willing to buy the item at the highest price.

[1584] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[1585] Step 13:

[1586] The user proceeds with the transaction using the provided merchant or personal contact information.

[1587] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[1588] Example 1

[1589] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1590] Conventional systems make it difficult for users to sell items efficiently at the best price. Specifically, users must contact multiple buyers and individuals and compare prices, which takes time and effort, and often results in the user being unable to find the best price. Therefore, there is a need for a system that allows users to easily and quickly find the buyer or individual who offers the highest purchase price.

[1591] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1592] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for packaging the input detailed information and image data and transmitting the packaged data to the server, means for separating the received text data from the image data, means for transmitting the image data to an external generative AI service and extracting item features, means for integrating the extracted features and detailed information to generate an item profile, means for storing the generated item profile in a database, means for searching for buyers or individuals based on the item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, and means for notifying the user of detailed information about the identified buyer or individual. This allows users to easily and quickly find the buyer or individual offering the highest purchase price.

[1593] "User" means an individual or corporation that wishes to sell items using the system.

[1594] "Detailed item information" refers to various information entered by the user, such as the item's name, brand, model number, and condition.

[1595] "Item image data" refers to a photo of an item that a user takes and uploads to the system.

[1596] "Packaging" is the process of combining detailed information and image data of an item into a single data format (e.g., JSON format).

[1597] "Text data" is the text portion of the detailed information about an item entered by the user.

[1598] "Image data" refers to the image file uploaded as a photo of the item.

[1599] A "generative AI service" is an external artificial intelligence service that performs image analysis, etc.

[1600] "Feature extraction" means deriving information such as the attributes and condition of an item from image data.

[1601] An "item profile" is data that integrates detailed information about an item with extracted features.

[1602] A "database" is a data storage system that stores and manages item profiles, user information, etc.

[1603] A "buyer or individual" is a business or individual willing to purchase an item.

[1604] "Search" is the process of locating buyers or individuals within the database based on an item profile.

[1605] The "purchase price" is the purchase price offered by a buyer or individual for the item.

[1606] "Notification" is the act of notifying a user of specified information.

[1607] The present invention is a system that allows users to sell their items efficiently at the optimal price. This system extracts item characteristics based on detailed information and image data provided by the user, and generates an item profile. It then automatically searches for buyers and individuals based on the item profile, identifies the buyer or individual who offers the highest purchase price, and notifies the user of that information.

[1608] Hardware and software used

[1609] User device: A device such as a smartphone or PC where a user inputs information. This device is built using web forms using HTML, CSS, and JavaScript, or a mobile app using React Native.

[1610] Server: The central system that receives input information, analyzes data, and communicates with generative AI services. Scripts are executed using Python or Node.js.

[1611] Data storage system: Databases such as Amazon RDS and MongoDB are used to store information.

[1612] Generative AI services: Google Cloud Vision API and other services are used to analyze image data.

[1613] Program processing

[1614] 1. Using a web form or mobile app, users enter detailed information about the item they want to sell (such as name, brand, model number, and condition), and also take a photo of the item with a smartphone or digital camera and upload it to the system as image data.

[1615] 2. The device packages the input text data and image data in JSON format and sends it to the server via an HTTP POST request.

[1616] 3. The server receives the received data using the API gateway and separates the text data from the image data using a data parser.

[1617] 4. The server sends the image data to a generative AI service, which extracts item features (e.g., label information, tags, metadata) from the image data.

[1618] 5. The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[1619] 6. The server searches a database of buyers and individuals based on the item profile, and uses a sorting algorithm to identify the buyer or individual offering the highest price.

[1620] 7. The server compiles detailed information about the identified businesses and individuals (e.g., name, contact information, purchase price) into a JSON response and sends it to the user's device.

[1621] 8. The terminal parses the received response data and displays it visually on the user's device, allowing the user to contact the seller and sell the item.

[1622] Examples and prompts

[1623] Case: Selling an old digital camera "Sony α6000"

[1624] 1. The user opens the smartphone app, enters the brand "Sony," the model number "α6000," and the condition "good," then takes a photo with the camera and uploads it.

[1625] 2. The device compiles the input text information and image data in JSON format and sends it to the server using an HTTP POST request.

[1626] 3. The server analyzes the received data and sends the image data to a generative AI service, which extracts features such as "Sony α6000," "with optical zoom lens," and "some scratches."

[1627] 4. The server integrates the extracted features with the input information to generate an item profile and save it in the database.

[1628] 5. The server searches a database of buyers and individuals based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1629] 6. The server notifies the user of the details of the identified vendor.

[1630] 7. The terminal displays the received information about the supplier, and the user contacts the supplier based on that information.

[1631] Prompt Sentence Examples

[1632] Upload an image of your Sony α6000 and enter details such as its condition and model number. Our system will search for the best buyers and notify you of the highest bidder.

[1633] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1634] Step 1:

[1635] Users launch a web form or mobile app, enter details about the item they want to sell (such as name, brand, model number, and condition), and take and upload a photo of the item.

[1636] Input: Item text information, item image data

[1637] How it works: Using a smartphone or digital camera, the user enters text information, takes a photo, and uploads the image data.

[1638] Output: Input and image upload completion status on the user's device

[1639] Step 2:

[1640] The device packages the entered text information and image data in JSON format and sends it to the server via an HTTP POST request.

[1641] Input: User-entered text information, item image data

[1642] How it works: Using JavaScript and React Native, the input form data and image files are converted into JSON format and an HTTP POST request is created.

[1643] Output: Request data in JSON format, status of sending to server

[1644] Step 3:

[1645] The server receives the received data using an API gateway and begins separating and analyzing it, parsing and separating text data and image data.

[1646] Input: Request data in JSON format

[1647] How it works: Using Python and Node.js scripts, JSON data is parsed and separated into text data and image data.

[1648] Output: Separated text data, separated image data

[1649] Step 4:

[1650] The server sends the image data to a generative AI service, which extracts the item's features.

[1651] Input: Separated image data

[1652] How it works: Sends an API request to a generative AI service (e.g., Google Cloud Vision API) to perform feature extraction processing.

[1653] Output: Feature data from generative AI services (e.g., label information, tags, metadata)

[1654] Step 5:

[1655] The server integrates the extracted features with the input text information to generate an item profile.

[1656] Input: separated text data, feature data from generative AI services

[1657] Operation: Using a data structure, text data and feature data are integrated and organized into item profiles.

[1658] Output: Item profile (integrated data)

[1659] Step 6:

[1660] The server stores the generated item profile in a database.

[1661] Input: Item Profile

[1662] How it works: Inserts item profiles into a database (e.g., Amazon RDS, MongoDB) using a SQL or NoSQL query.

[1663] Output: Item profile saved in database, save status

[1664] Step 7:

[1665] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual who offers the highest desired purchase price.

[1666] Input: Item Profile

[1667] How it works: It uses SQL or NoSQL query languages ​​to search for businesses and individuals in a database, then uses a sorting algorithm to identify those offering the best buyback price.

[1668] Output: Details of the businesses and individuals offering the highest purchase prices

[1669] Step 8:

[1670] The server creates a response to notify the user of detailed information about the identified business or individual, and sends it to the terminal.

[1671] Input: Details of the company or individual offering the highest purchase price

[1672] Operation: Creates response data in JSON format and sends it to the user's device via an HTTP POST or GET request.

[1673] Output: JSON format response data, sending status

[1674] Step 9:

[1675] The terminal parses the response data received from the server and displays it visually on the user's device, allowing the user to contact the merchant based on that information.

[1676] Input: JSON format response data from the server

[1677] What it does: Parses the response data and displays it in a user interface using React Native or another UI framework, visually rendering the information for the user to see.

[1678] Output: details of the business or individual displayed to the user, and the user's actions (e.g., contact)

[1679] (Application example 1)

[1680] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1681] Currently, in order to sell their items at a high price, users must compare multiple buyers, which takes time and effort. It is also difficult to determine whether the purchase price is fair. In particular, extracting item features using image data requires specialized knowledge and high-performance analytical technology, which is difficult for average users. This can result in users missing out on opportunities to sell their items at the optimal price. There is a need to solve these problems.

[1682] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1683] In this invention, the server includes means for receiving detailed item information from a user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for generating an item profile by integrating the extracted features and detailed information, means for searching for buyers or individuals based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for the user to input detailed item information and image data using a mobile device, thereby enabling the user to easily sell items at the optimal price.

[1684] A "User" is any person or entity that seeks to sell an item using the System.

[1685] "Detailed item information" refers to information such as the name, brand, model number, and condition of the item being sold.

[1686] "Item image data" refers to the photograph or image file data of the item you are selling.

[1687] "Features of an item" are specific attributes and characteristics of an item extracted by analyzing image data.

[1688] An "item profile" is a data set that integrates detailed information and characteristics of an item.

[1689] A "buyer or individual" is a business or individual willing to purchase an item from a user.

[1690] A "mobile device" is a portable information processing device such as a smartphone or tablet.

[1691] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze image data and extract features.

[1692] "Searching" is the process of searching through data in a database to find information that meets specific criteria.

[1693] A "notification" is a message or alert sent to inform a user of specific information.

[1694] System Overview

[1695] The basic function of the invention is a system that helps users sell their items efficiently at the best price. Users input detailed information about the item and upload image data, and the system automatically searches for buyers and individuals and identifies the one offering the highest purchase price.

[1696] Hardware and software used

[1697] Hardware: Smartphone (iOS or Android), server

[1698] Software: Python, Flask (web framework), PIL (image processing library), generative AI model API

[1699] 1. Receiving data

[1700] Using a smartphone app, users input detailed information about the items they want to sell (e.g., name, brand, model number, condition, etc.) and also take and upload photos of the items to provide image data to the system.

[1701] 2. Data Analysis

[1702] The device sends the input details and image data to a server, which then sends the image data to a generative AI model to extract specific features. This generative AI model then uses machine learning and deep learning to analyze the image.

[1703] 3. Generate Item Profiles

[1704] The server combines the extracted features and detailed information to generate an item profile, which is then stored in a database.

[1705] 4. Search for buyers and individuals

[1706] Based on the item profile, the server searches a database of buyers and individuals, and from the search results, identifies the buyer or individual offering the highest purchase price.

[1707] 5. Notification of results

[1708] The system formats and notifies the user of the details of the identified businesses and individuals, allowing the user to check the best buyback price information on the smartphone app and contact the businesses or individuals.

[1709] Example

[1710] For example, if a user wants to sell their old digital camera, a Sony α6000, they can use the system as follows:

[1711] 1. The user opens the smartphone app and enters the camera's details (brand "Sony," model number "α6000," condition "good").

[1712] 2. Take a photo with the camera and upload the image data through the app.

[1713] 3. The app sends the input information and image data to the server.

[1714] 4. The server sends the image data to a generative AI service to extract the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1715] 5. The extracted features are integrated with the input information to generate an item profile, which is then stored in the database.

[1716] 6. Based on the item profile, a database of buyers and individuals is searched to identify the buyer offering the highest purchase price.

[1717] 7. The details of the best fit supplier will be sent to the user's smartphone app.

[1718] Prompt Sentence Examples

[1719] An example of a prompt to input to a generative AI model is as follows:

[1720] "Extract item characteristics from the provided image file. These characteristics should include the item's name, brand, model number, condition, and any special notes."

[1721] This allows users to easily sell their digital cameras and other items through the system for the highest possible price.

[1722] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1723] Step 1:

[1724] Using a smartphone app, users enter detailed information about the item they want to sell (e.g., name, brand, model number, condition) and upload image data of the item. Specifically, they enter the required text data (name, brand, model number, condition) into the app's form, take a photo or select an image from the gallery, and press the upload button. The entered detailed information and image data are temporarily stored in the app's local storage and then sent to the server for subsequent processing.

[1725] Step 2:

[1726] The device packages the details entered by the user and the uploaded image data and sends them to the server. The details are sent in JSON format, and the image data is sent in binary format. The server receives this data and prepares it for the next analysis process.

[1727] Step 3:

[1728] The server sends the received image data to the generative AI model API to extract the item's features. The image data is passed to the generative AI model using the prompt, "Please extract the item's features from the specified image file. The features should include the item's name, brand, model number, condition, and any special notes." During this process, the generative AI model uses machine learning and deep learning algorithms to analyze the image and return the extracted feature information (e.g., name "Sony α6000," condition "good," special notes "with optical zoom lens, some scratches").

[1729] Step 4:

[1730] The server combines the extracted feature information with the detailed information entered by the user to generate an item profile. Specifically, the feature information and detailed information are merged into a single JSON object, which is then saved in the database as the item profile. This provides a clear overall picture of the item.

[1731] Step 5:

[1732] The server searches for buyers or individuals based on the generated item profile. The server queries the buyer or individual database using the item's category and characteristics as keys to obtain a list of candidates. The server also obtains the desired purchase price for each candidate.

[1733] Step 6:

[1734] The server identifies the company or individual who offers the highest purchase price from the list of candidates. The server compares the purchase prices and extracts detailed information (e.g., name, contact information, purchase price) of the company or individual who offered the highest price. This determines the most advantageous seller for the user.

[1735] Step 7:

[1736] The server formats and sends detailed information about the identified sellers or individuals to the user's device, including the seller's or individual's name, contact information, and purchase price, allowing the user to identify the most suitable seller or individual within the app and contact them based on that information.

[1737] Through the above processing steps, users can easily sell items at the best price.

[1738] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1739] System Overview

[1740] The present invention is a system that allows users to efficiently sell their items at the optimal price. By entering detailed information about the item and uploading image data, users can use the system to automatically search for buyers and individuals and identify the person or business offering the highest purchase price. Furthermore, the present invention adds a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[1741] Program processing description

[1742] Step 1: Accepting user input

[1743] 1. A user uses a web form or mobile app to enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1744] 2. The user takes a photo of the item and uploads it to the system.

[1745] Step 2: Receiving and analyzing data

[1746] 1. The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1747] 2. The server analyzes the received request and separates the text data from the image data.

[1748] 3. The server sends the image data to an external generative AI service to extract the item's features.

[1749] Step 3: Generate an item profile

[1750] 1. The server generates an item profile based on the extracted features and integrates them with the input details.

[1751] 2. The server stores the item profile in a database.

[1752] Step 4: Search and identify buyers and individuals

[1753] 1. The server searches a database of buyers and individuals based on the item profile.

[1754] 2. The server identifies the business or individual offering the highest purchase price from the search results.

[1755] Step 5: Use the Emotion Engine

[1756] 1. The server launches the emotion engine to recognize the user's emotional state.

[1757] 2. The emotion engine analyzes user input and behavioral data to assess the user's current emotional state.

[1758] 3. The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[1759] Step 6: Notification of results

[1760] 1. The server formats the details of the identified merchant or individual (such as name, contact information, and offered price) and notifies the user.

[1761] 2. The terminal displays the data received from the server on the user's device so that the user can check the information.

[1762] Specific examples

[1763] Case: Selling old digital cameras and emotion recognition

[1764] 1. A user opens the app to sell a Sony a6000.

[1765] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[1766] 3. The terminal sends the input information and image data to the server.

[1767] 4. The server sends the image data to a generative AI that extracts the item's characteristics (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1768] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[1769] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[1770] 7. The server utilizes an emotion engine to assess the user's emotional state.

[1771] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[1772] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[1773] 10. The server formats the identified vendor information and notifies the user.

[1774] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[1775] In this way, by using the system based on the present invention, users can easily find businesses or individuals that will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[1776] The processing flow will be explained below.

[1777] Step 1:

[1778] Users open a web form or mobile app and enter details about the item they want to sell (such as name, brand, model number, condition, etc.).

[1779] What happens: The user enters data into form fields and presses the "Submit" button.

[1780] Step 2:

[1781] Users take photos of items and upload them to the system.

[1782] Specific behavior: The user takes a photo using the camera, selects the image from the file selection dialog, and presses the upload button.

[1783] Step 3:

[1784] The device packages the entered details and image data and sends it to the server via an HTTP POST request.

[1785] Specific operation: The device combines the text data and image data into a single JSON object and sends it to the specified server URL.

[1786] Step 4:

[1787] The server analyzes the received request and separates the text data from the image data.

[1788] What happens: The server parses the request body and stores the text and image fields separately.

[1789] Step 5:

[1790] The server sends the image data to the generative AI via an HTTP request and requests analysis.

[1791] Specific behavior: Constructs and sends a request to encode image data and send it to an external API.

[1792] Step 6:

[1793] Generative AI performs image analysis and extracts item features (brand, model number, condition, etc.).

[1794] Specific operation: The generative AI analyzes the received image and returns the identified features in JSON format to the server.

[1795] Step 7:

[1796] The server receives the response from the generative AI and integrates it with the text data to generate an item profile.

[1797] Specific operation: The server analyzes the JSON response from the generative AI and adds it to the text data to create an item profile.

[1798] Step 8:

[1799] The server stores the generated item profile in a database.

[1800] Specific behavior: Inserts the item profile into the appropriate table in the database.

[1801] Step 9:

[1802] The server generates a query to search for buyers or individuals based on the item profile.

[1803] What it does: Creates an SQL query using data from the item profile and runs it against the database.

[1804] Step 10:

[1805] The server identifies the trader or individual offering the highest purchase price from the search results.

[1806] Specific behavior: Sorts search results by price and selects the entry offering the highest price.

[1807] Step 11:

[1808] The server activates an emotion engine to recognize the user's emotional state.

[1809] Specific operation: Sends user input data and behavioral data to the emotion engine and requests emotion analysis.

[1810] Step 12:

[1811] The emotion engine analyzes the user's emotional state and returns the results to the server.

[1812] Specific operation: The emotion engine analyzes the user's text input and behavioral data, generates emotion labels such as "satisfied" or "anxiety," and returns them to the server.

[1813] Step 13:

[1814] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine.

[1815] Specific operation: An algorithm is applied that dynamically changes notification messages and suggestions depending on the user's emotional state.

[1816] Step 14:

[1817] The server formats the details of the identified business or individual and notifies the user.

[1818] Specific operation: Detailed information is structured in JSON format and sent to the user's device as an HTTP response.

[1819] Step 15:

[1820] The terminal displays the data received from the server on the user's device, displaying detailed information about businesses and individuals willing to buy the items at the highest price.

[1821] Specific behavior: Parses the received JSON data and embeds the information in the UI component.

[1822] Step 16:

[1823] The user contacts businesses and individuals based on the information provided.

[1824] Specific actions: Using the provided contact information, contact the buyer or individual by phone or email.

[1825] Example 2

[1826] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1827] Currently, systems that allow users to sell items efficiently and at optimal prices require simple and fast procedures for data entry and searching for buyers. However, conventional systems have had difficulty automating these procedures and providing feedback that reflects the user's emotional state. Furthermore, they often lack functionality to enhance user satisfaction throughout the entire selling process.

[1828] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for a buyer or individual based on the generated item profile, means for identifying the buyer or individual offering the highest desired purchase price from the search results, means for notifying the user of detailed information about the identified buyer or individual, and means for recognizing the user's emotional state and adjusting the notification content and suggestions. This allows the user to efficiently sell items at the highest price, and furthermore, by receiving feedback based on emotional recognition, the user can achieve a better user experience and greater satisfaction.

[1829] "User" means an individual or corporation that wishes to sell items using the system.

[1830] "Detailed item information" refers to attribute information such as the name, brand, model number, and condition of the item being sold.

[1831] "Item image data" refers to a photo or image file of the item being sold.

[1832] A "generative AI model" is an algorithm or system that extracts specific patterns or features from data provided to it.

[1833] A "prompt sentence" is an input sentence that instructs an AI model for a specific purpose.

[1834] "Item features" refers to information about the appearance and condition of an item extracted through image analysis.

[1835] An "item profile" is a data set that integrates detailed information and feature information about an item.

[1836] A "buyer or individual" is a business or individual who is willing to buy items that a user wishes to sell.

[1837] An "emotion engine" is a software module for analyzing and assessing a user's emotional state.

[1838] The "means for adjusting notification content and suggestions" is a mechanism that has the function of dynamically changing notification content and suggestions based on the user's emotional state.

[1839] The present invention is a system that allows users to efficiently sell their items at the optimal price. The system automatically searches for buyers and individuals by allowing users to input detailed information about the items and upload image data, and identifies the person or seller offering the highest purchase price. The system also has a function that recognizes the user's emotions and adjusts notification content and suggestions to improve the user experience.

[1840] Hardware and software used

[1841] Hardware

[1842] 1. Device: The smartphone, tablet, or computer used by the user.

[1843] 2. Server: A cloud server for data analysis, search, notifications, etc.

[1844] software

[1845] 1. Web form or mobile app: An interface for users to enter and upload item details and image data.

[1846] 2. Generative AI models: Used to extract features from image data of items. Specifically, Azure Computer Vision API and Google Cloud Vision API are examples.

[1847] 3. Emotion engine: Software for analyzing the user's emotional state, such as Amazon Rekognition or Microsoft Azure Emotion API.

[1848] 4. Database: A storage system for storing detailed item information, image data, extracted features, item profiles, buyer information, etc.

[1849] Data processing and calculation

[1850] Data Entry

[1851] Users access the system using a terminal, enter details of the item they want to sell (such as name, brand, model number, and condition) through a web form or mobile app, and take photos of the item using a smartphone or camera and upload them to the system.

[1852] Data Packaging and Transmission

[1853] The device packages the entered details and image data. This package contains text data in JSON format and Base64-encoded image data. This data is sent to the server via an HTTP POST request.

[1854] Data analysis

[1855] The server analyzes the received package. First, it parses the JSON data to separate it into text data and image data. Next, it creates a prompt to send to the generative AI model. An example of a specific prompt would be, "Please extract the features of the item shown in this image."

[1856] Item Profile Generation

[1857] The server receives the feature information returned by the generative AI model (e.g., "Sony α6000," "with optical zoom lens," "some scratches") and combines it with the detailed information entered by the user to generate an item profile, which is then stored in an SQL database.

[1858] Search and identify buyers and individuals

[1859] The server searches a database of buyers and individuals based on the item profile and identifies the buyer or individual offering the highest purchase price. The search is performed using SQL or NoSQL queries.

[1860] Emotional state analysis

[1861] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavioral data, such as keystroke speed and microexpressions, to evaluate the user's current emotional state. Based on the evaluation results, the notification content and suggestions are adjusted. For example, the content may be adjusted to say, "Don't worry. We've found the best buy price for you."

[1862] Notification of results

[1863] The server formats and notifies the user of the details of the identified merchant or individual (such as name, contact information, and offered price). This data is sent to the user's device in HTML or JSON format. The device displays the data received from the server on the user's device, allowing the user to review the information and contact the merchant if necessary.

[1864] Specific examples

[1865] Case: Selling old digital cameras and emotion recognition

[1866] 1. A user opens the app to sell a Sony a6000.

[1867] 2. The user enters the brand "Sony," the model number "α6000," and the condition "good," and uploads a photo of the camera.

[1868] 3. The terminal sends the input information and image data to the server.

[1869] 4. The server sends the image data to a generative AI that extracts the item's features (e.g., "Sony α6000," "with optical zoom lens," "some scratches").

[1870] 5. The server integrates the extracted features with the input information to generate an item profile and stores it in a database.

[1871] 6. The server searches the database of buyers and individuals based on the item profile and identifies the buyer offering the highest desired purchase price (e.g., 50,000 yen).

[1872] 7. The server utilizes an emotion engine to assess the user's emotional state.

[1873] 8. The emotion engine evaluates emotions such as "satisfaction" or "anxiety" based on the user's actions and input, and returns the results to the server.

[1874] 9. The server adjusts the notification content based on the results of the emotion engine (e.g., "Congratulations, you found the best buy price!" if a happy emotion is recognized).

[1875] 10. The server formats the identified vendor information and notifies the user.

[1876] 11. The terminal displays the information about the vendor received from the server, and the user contacts the vendor based on that information.

[1877] As described above, by using the system based on the present invention, users can easily find businesses or individuals who will buy their items at the highest price, and can also provide a better user experience by providing notifications and suggestions that take the user's emotions into consideration.

[1878] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1879] Step 1: Receiving User Input

[1880] Users access the system using a web form or mobile app. They enter details of the item they want to sell (such as name, brand, model number, and condition), and they also take a photo of the item and upload the image data to the system.

[1881] Input: Item details, image data

[1882] Output: User input data (details and image data)

[1883] Step 2: Packaging and sending the data

[1884] The device packages the detailed information and image data entered by the user. This package contains text data in JSON format and Base64-encoded image data. The device sends this package to the server via an HTTP POST request.

[1885] Input: User-entered data (details and image data)

[1886] Output: HTTP POST request (details in JSON format and Base64 encoded image data)

[1887] Step 3: Receiving and analyzing data

[1888] The server analyzes the received HTTP POST request. It parses the JSON data to separate the text data from the image data. It then creates a prompt to analyze the image data. The specific prompt is "Please extract the characteristics of the item shown in this image."

[1889] Input: HTTP POST request (detailed information in JSON format and Base64 encoded image data)

[1890] Output: Text data, image data, prompt

[1891] Step 4: Analyzing the image data

[1892] The server sends the image data to a generative AI model, which extracts the item's features from the image using the Azure Computer Vision API or Google Cloud Vision API.

[1893] Input: Image data, prompt ("Please extract the features of the item in this image.")

[1894] Output: Characteristic information (e.g. "Sony α6000", "with optical zoom lens", "some scratches")

[1895] Step 5: Generate item profiles

[1896] The server combines the feature information returned by the generative AI model with the details entered by the user to generate an item profile, which is then stored in an SQL database.

[1897] Input: characteristic information, detailed information

[1898] Output: Item Profiles (records stored in a SQL database)

[1899] Step 6: Search the database of buyers and individuals

[1900] The server searches a database of buyers and individuals based on the generated item profile. This search uses SQL or NoSQL queries to identify the buyer or individual offering the highest purchase price.

[1901] Input: Item Profile

[1902] Output: List of buyers and individuals, the buyer or individual who offers the highest purchase price

[1903] Step 7: Analyze emotional state

[1904] The server activates an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input and behavior data and evaluates the user's current emotional state.

[1905] Input: User input data, behavioral data

[1906] Output: Emotion evaluation result (e.g., "Satisfied" or "Anxious")

[1907] Step 8: Adjust your notifications

[1908] The server adjusts the notification content and suggestions based on the evaluation results from the emotion engine. For example, if the user is rated as "anxious," the content will be adjusted to something like "Don't worry. We've found the best buy price for you."

[1909] Input: Emotion evaluation result

[1910] Output: Adjusted notification content

[1911] Step 9: Notification of results

[1912] The server formats and transmits the details of the identified merchant or individual (such as name, contact details, and offered price) to the user's terminal.

[1913] Input: Business or individual details, tailored notification content

[1914] Output: A message to inform the user

[1915] Step 10: Display to the User

[1916] The terminal displays the data received from the server on the user's device, allowing the user to contact a seller and sell the item at the highest price possible.

[1917] Input: A notification message to the user

[1918] Output: Displayed notification content

[1919] The above is the specific flow of program processing in the system of the present invention.

[1920] (Application example 2)

[1921] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1922] In conventional item selling systems, users had to spend a lot of time and effort to find the most suitable buyer. Furthermore, because notifications and suggestions were made uniformly without taking the user's emotional state into consideration, it was difficult to improve the user experience. Furthermore, when analyzing image data, it was difficult to extract features with high accuracy, and accurate evaluation of items was often impossible. This reduced the reliability and usability of the system.

[1923] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1924] In this invention, the server includes means for receiving detailed item information from the user, means for receiving image data of the item from the user, means for analyzing the received image data to extract item features, means for integrating the extracted features and detailed information to generate an item profile, means for searching for potential buyers based on the generated item profile, means for identifying the potential buyer offering the highest desired purchase price from the search results, means for notifying the user of detailed information of the identified potential buyer, and means for recognizing the user's emotional state and adjusting the notification content and proposals based on that state. This allows the user to efficiently find the most suitable potential buyer, and the user experience is improved by responding in consideration of emotions.

[1925] A "user" is someone who uses this system to provide detailed information and image data about an item and find the most suitable buyer.

[1926] "Detailed item information" refers to specific information about the item being sold, such as the name, brand, model number, and condition.

[1927] "Image data" refers to photographs or images of the items being sold, which visually indicate the condition and characteristics of the items.

[1928] "Item characteristics" refers to the characteristics and attributes related to the appearance and condition of an item that are obtained by analyzing image data.

[1929] An "item profile" is a comprehensive description of an item that is generated by integrating user-supplied details with analyzed image data features.

[1930] "Purchaser" refers to a purchasing company or individual who wishes to purchase an item.

[1931] "Emotional state" refers to the emotional state that a user shows while using the system, and includes emotional expressions such as joy and anxiety.

[1932] "Notification content" refers to the method and message used to convey to the user detailed information about the most suitable buyer and the desired purchase price.

[1933] "Suggestions" refer to specific actions or information that are based on the user's emotional state and are intended to improve the user experience.

[1934] The present invention provides a system that allows users to sell their items efficiently at optimal prices. The system improves the user experience by recognizing the user's emotional state and adjusting notifications and suggestions accordingly. Specific embodiments of the present invention are described below.

[1935] System Configuration

[1936] This system mainly consists of the following components:

[1937] 1. User device: The device used by the user, such as a smartphone or computer.

[1938] 2. Server: A central system that processes information and connects to a database of potential buyers.

[1939] 3. Generative AI services: External services for analyzing image data, such as Google Cloud Vision API or IBM Watson Visual Recognition.

[1940] 4. Emotion recognition engine: Software for assessing the user's emotional state, such as the Affectiva SDK or Microsoft Azure Emotion API.

[1941] Program processing flow

[1942] Receiving User Input

[1943] The user inputs detailed information (such as name, brand, model number, and condition) of the item they wish to sell using a user terminal. They also take a picture of the item and upload it to the system.

[1944] Data reception and analysis

[1945] The user device sends the entered detailed information and image data to the server. The server analyzes the data and processes the text and image data. The image data is sent to the generation AI service, which extracts the item's features.

[1946] Generate an item profile

[1947] The server combines the extracted features with the input details to generate an item profile, which is then stored in a database.

[1948] Searching for and identifying potential buyers

[1949] The server searches a database of potential buyers based on the item profile and identifies the potential buyer offering the highest desired purchase price.

[1950] Use of emotion engine

[1951] The server activates an emotion recognition engine to recognize the user's emotional state. It analyzes the user's input and behavior data and evaluates the emotional state.

[1952] Notification of results

[1953] The server notifies the user of the details of the identified potential buyers (such as their names, contact information, and asking prices). The content of the notification is adjusted based on the evaluation results from the emotion recognition engine.

[1954] Specific examples

[1955] For example, if a user wants to sell an Apple iPhone 12, the system works as follows:

[1956] 1. The user enters "iPhone 12, Brand: Apple, Model: A2172, Condition: Good" and uploads a photo of the item.

[1957] 2. The user terminal sends the input information and image data to the server.

[1958] 3. The server analyzes the image using the Google Cloud Vision API and extracts features such as "iPhone 12," "no scratches," and "good working condition."

[1959] 4. The server integrates the extracted features and detailed information to generate an item profile and stores it in a database.

[1960] 5. The server searches the database of potential buyers based on the item profile and identifies the buyer offering the highest purchase price (e.g., 50,000 yen).

[1961] 6. The emotion engine assesses the user's emotional state and recognizes emotions such as joy and anxiety.

[1962] 7. The server adjusts the notification content based on the results of the emotion engine (e.g., if a happy emotion is recognized, it displays "Congratulations, we found the best buy price!").

[1963] 8. The server formats the details of the identified buyers and notifies the user.

[1964] Prompt Sentence Examples

[1965] Please tell us the name of the item you are trying to sell.

[1966] "Please enter the item brand and model number"

[1967] "What is the current condition of the item (new, good condition, used, etc.)?"

[1968] Upload a photo of the item

[1969] In this way, users can efficiently find the best buyers, and emotional responses provide a better user experience.

[1970] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1971] Step 1:

[1972] The user enters and uploads detailed information and image data for the item.

[1973] Using a user device (smartphone or PC), the user inputs detailed information about the item they want to sell, such as the name, brand, model number, and condition, and then takes or selects and uploads a photo of the item. Detailed information and image data are obtained as user input. This information is output as a temporary save on the device.

[1974] Step 2:

[1975] The device sends the input information and image data to the server.

[1976] The terminal packages the details and image data entered by the user and sends it to the server via an HTTP POST request. The input is the details and image data entered by the user, and the output is the transmission to the server.

[1977] Step 3:

[1978] The server receives and analyzes the data

[1979] The server receives data sent from the device and separates it into text data and image data. The image data is sent to an external AI generation service (Google Cloud Vision API or IBM Watson Visual Recognition) to extract its features. The input is data from the device, and the output is separated text data and image data with extracted features.

[1980] Step 4:

[1981] The server generates and saves the item profile.

[1982] The server integrates the extracted features with the text data to generate an item profile, which is then stored in a database. The input is the separated text data and extracted features, and the output is the item profile.

[1983] Step 5:

[1984] The server searches and identifies potential buyers

[1985] The server searches the database of potential buyers based on the item profile stored in the database. It identifies the potential buyer who offers the highest purchase price. The input is the item profile, and the output is the details of the most suitable potential buyer.

[1986] Step 6:

[1987] The server uses an emotion engine to evaluate the user's emotional state.

[1988] The server uses an emotion recognition engine (Affectiva SDK or Microsoft Azure Emotion API) to analyze the user's input and behavioral data and evaluate their emotional state. The input is the user's behavioral data, and the output is the evaluated user's emotional state.

[1989] Step 7:

[1990] The server adjusts the notification content and notifies the user.

[1991] The server adjusts the notification content and proposals based on the evaluation results and notifies the user of the details of the identified potential buyers. The notification content is adjusted based on the user's emotional state. The input is the details of the identified potential buyers and the evaluated emotional state, and the output is a notification message.

[1992] Step 8:

[1993] The device displays the notification from the server to the user.

[1994] The terminal displays the notification message received from the server to the user. The user can contact the potential buyer based on this information. The input is the notification message from the server, and the output is the display to the user.

[1995] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1996] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1997] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1998] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1999] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2000] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2001] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2002] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2003] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2004] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2005] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2006] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2007] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2008] 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.

[2009] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2010] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2011] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2012] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2013] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2014] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2015] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2016] The following is further disclosed regarding the above embodiment.

[2017] (Claim 1)

[2018] means for receiving detailed item information from a user;

[2019] means for receiving image data of an item from a user;

[2020] means for analyzing the received image data to extract characteristics of the item;

[2021] a means for integrating the extracted features with detailed information to generate an item profile;

[2022] A means for searching for buyers or individuals based on the generated item profile;

[2023] A means to identify the company or individual offering the highest purchase price from the search results, and

[2024] a means for notifying the user of details of the identified buyer or individual;

[2025] A system including:

[2026] (Claim 2)

[2027] 10. The system of claim 1, further comprising means for utilizing an external service to analyze image data of the item.

[2028] (Claim 3)

[2029] 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database.

[2030] "Example 1"

[2031] (Claim 1)

[2032] means for receiving detailed item information from a user;

[2033] means for receiving image data of an item from a user;

[2034] means for packaging the input detailed information and image data and transmitting the packaged data to a server;

[2035] means for separating the received text data from the image data;

[2036] A means of transmitting image data to an external generative AI service to extract item features;

[2037] a means for integrating the extracted features with detailed information to generate an item profile;

[2038] a means for storing the generated item profile in a database;

[2039] A means to search for buyers or individuals based on item profiles;

[2040] A means to identify the company or individual offering the highest purchase price from the search results, and

[2041] a means for notifying the user of details of the identified buyer or individual;

[2042] A system including:

[2043] (Claim 2)

[2044] 10. The system of claim 1, further comprising means for utilizing an external service to analyze image data of the item.

[2045] (Claim 3)

[2046] 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database.

[2047] "Application Example 1"

[2048] (Claim 1)

[2049] means for receiving detailed item information from a user;

[2050] means for receiving image data of an item from a user;

[2051] means for analyzing the received image data to extract characteristics of the item;

[2052] a means for integrating the extracted features with detailed information to generate an item profile;

[2053] A means for searching for buyers or individuals based on the generated item profile;

[2054] A means to identify the company or individual offering the highest purchase price from the search results, and

[2055] a means for notifying the user of details of the identified buyer or individual;

[2056] a means for a user to input detailed item information and image data using a mobile device;

[2057] A system including:

[2058] (Claim 2)

[2059] 10. The system of claim 1, further comprising means for utilizing a generative AI model to analyze image data of the item.

[2060] (Claim 3)

[2061] 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database.

[2062] "Example 2: Combining Emotion Engines"

[2063] (Claim 1)

[2064] means for receiving detailed item information from a user;

[2065] means for receiving image data of an item from a user;

[2066] means for analyzing the received image data to extract characteristics of the item;

[2067] a means for integrating the extracted features with detailed information to generate an item profile;

[2068] A means for searching for buyers or individuals based on the generated item profile;

[2069] A means to identify the company or individual offering the highest purchase price from the search results, and

[2070] a means for notifying the user of details of the identified buyer or individual;

[2071] A means for recognizing the user's emotional state and tailoring notifications and suggestions;

[2072] A system including:

[2073] (Claim 2)

[2074] 10. The system of claim 1, further comprising means for utilizing an external service to analyze image data of the item.

[2075] (Claim 3)

[2076] 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database.

[2077] "Application example 2 when combining emotion engines"

[2078] (Claim 1)

[2079] means for receiving detailed item information from a user;

[2080] means for receiving image data of an item from a user;

[2081] means for analyzing the received image data to extract characteristics of the item;

[2082] a means for integrating the extracted features with detailed information to generate an item profile;

[2083] A means for searching for buyers based on the generated item profile;

[2084] A means for identifying the buyer who offers the highest purchase price from the search results;

[2085] a means for notifying a user of detailed information of the identified potential buyer;

[2086] means for recognizing a user's emotional state and tailoring notifications and suggestions based on that state;

[2087] A system including:

[2088] (Claim 2)

[2089] 10. The system of claim 1, further comprising means for utilizing an external service to analyze image data of the item.

[2090] (Claim 3)

[2091] 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database. [Explanation of symbols]

[2092] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving detailed item information from a user; means for receiving image data of an item from a user; means for analyzing the received image data to extract characteristics of the item; a means for integrating the extracted features with detailed information to generate an item profile; A means for searching for buyers or individuals based on the generated item profile; A means to identify the company or individual offering the highest purchase price from the search results, and a means for notifying the user of details of the identified buyer or individual; A system including:

2. The system of claim 1 , further comprising means for utilizing an external service to analyze image data of the item.

3. 10. The system of claim 1, further comprising means for storing the received detailed information and extracted features in a database.

Images