system

Abstract

To provide a system that enables a user to quickly and efficiently evaluate the value of an item. [Solution] A system including a photographing means for a user to photograph images of multiple items, a preprocessing means for preprocessing the photographed images, an identification means for individually identifying each item from the preprocessed images, a feature extraction means for extracting features of each identified item, a transmission means for transmitting the extracted features to a server, a search means for the server to search for corresponding information in a database based on the features and obtain purchase price information, and a display means for receiving the obtained purchase price information and displaying it to the user.

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] Currently, checking the buyback market value of items such as trading cards, books, and game software in the home requires time and effort, requiring individual searches. This makes it difficult to quickly determine the value of items that children no longer play with and decide whether to sell or dispose of them. In particular, when multiple items are mixed together, manually checking each item is impractical and inconvenient. This results in unnecessary items piling up in the home, wasting space. The present invention aims to solve these problems and provide a system that allows users to quickly and efficiently evaluate the value of items. [Means for solving the problem]

[0005] The present invention is a system including an imaging means for a user to take images of multiple items, a preprocessing means for preprocessing the captured images, an identification means for individually identifying each item from the preprocessed images, a feature extraction means for extracting feature quantities for each identified item, a transmission means for transmitting the extracted feature quantities to a server, a search means for the server to search for corresponding information in a database based on the feature quantities and obtain buyback price information, and a display means for receiving the obtained buyback price information and displaying it to the user. This system allows a user to quickly check the value of multiple items by taking a single image, facilitating effective disposal or sale decisions.

[0006] "User" refers to a person who uses this system to assess the value of an item and make decisions about selling or disposing of it.

[0007] "Photographing means" refers to a camera function or device that allows a user to take an image.

[0008] The "pre-processing means" refers to a function that performs pre-processing such as noise removal and light adjustment on the captured image.

[0009] "Identification means" refers to image recognition algorithms or software that uniquely identify each item from the pre-processed image.

[0010] The "feature extraction means" refers to a function for extracting detailed features such as character recognition, shape features, and color features from the identified item.

[0011] "Transmission means" refers to a communication function or method for transmitting extracted features to a server.

[0012] The term "server" refers to a central processing unit that receives the feature amount, searches the information in the database, and obtains the purchase price information.

[0013] The term "search means" refers to a function that searches for corresponding information in a database on the server and obtains purchase price information.

[0014] The "display means" refers to a user interface or display for displaying the acquired purchase price information to the user.

[0015] "Features" refers to information such as character recognition, shape features, and color features that are used to identify and evaluate items.

[0016] "Database" refers to a data storage device or system that stores information about items and purchase prices.

[0017] "Purchase price information" refers to information about the price offered by a dealer when selling an item. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. Specifically, this describes a system in which a user takes a photo and an AI automatically displays the market price for the item using the photo.

[0040] System Overview

[0041] The system consists of the following main components:

[0042] 1. A means for users to take pictures

[0043] 2. Preprocessing means for preprocessing the captured images

[0044] 3. A means of identifying each item individually from the preprocessed image.

[0045] 4. Feature extraction method to extract the features of each item

[0046] 5. A method for transmitting extracted features to the server

[0047] 6. A search method to search the database on the server and obtain purchase price information

[0048] 7. Display means for receiving the acquired price information and displaying it on the user interface

[0049] Program processing

[0050] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[0051] 2. The device receives the captured image and performs preprocessing, which includes noise reduction, image normalization, and light adjustment.

[0052] 3. The device analyzes the preprocessed image and uses a classification method to identify individual objects. This process uses an object detection algorithm (such as YOLO or Faster R-CNN), identifies the object's bounding box, and separates each object.

[0053] 4. The terminal extracts features from the identified items. Feature extraction methods include character recognition (OCR), shape features, and color features of the items.

[0054] 5. The device sends the extracted features to the server using a communication protocol over the Internet (e.g., HTTP or WebSocket).

[0055] 6. The server searches the database based on the received feature values. The search means uses the feature values ​​as a key to match the product information in the database and obtains the corresponding purchase price information.

[0056] 7. The server sends the acquired price information to the device, allowing the user to check the value of each item through the app.

[0057] 8. The terminal analyzes the received price information and prepares it for display on a user interface, including an interface that visually presents the price information to the user in a grid or list format.

[0058] Specific examples

[0059] Below are some specific usage examples.

[0060] Example: Determining the buyback price of a trading card

[0061] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0062] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[0063] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[0064] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[0065] 5. The device aggregates the extracted features and sends them to the server.

[0066] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[0067] 7. The server sends the acquired market price information to the terminal.

[0068] 8. The terminal displays the price information on the user interface and provides it to the user.

[0069] This flow allows users to quickly and easily check the buying price of trading cards and decide whether to sell them.

[0070] This system allows users to efficiently evaluate the value of multiple items at once, significantly reducing time and effort.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] Users launch the app and use the camera to take a photo of the item they want to sell. They can also take a photo of multiple items at once.

[0074] Step 2:

[0075] The device receives the captured image and performs pre-processing such as noise removal, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[0076] Step 3:

[0077] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, which generates a bounding box to uniquely identify each object.

[0078] Step 4:

[0079] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0080] Step 5:

[0081] The device compresses the extracted features into data packets and transmits them to a server over the Internet, using common communication protocols (e.g., HTTP or WebSocket) to ensure reliable and fast transmission.

[0082] Step 6:

[0083] The server analyzes the received feature data and searches the database. This search is performed by using the feature as a key to match the corresponding product information in the database.

[0084] Step 7:

[0085] The server retrieves market price information for the relevant items from its database and compiles it into a data packet, which identifies how much each item is worth.

[0086] Step 8:

[0087] The server sends the acquired market price information to the terminal, using a protocol (e.g., HTTPS) to ensure data integrity and communication stability.

[0088] Step 9:

[0089] The terminal analyzes the received market price information and prepares the data for display on the user interface, where the data is arranged in a format that is intuitively understandable to the user (for example, a grid or list format).

[0090] Step 10:

[0091] The terminal displays market price information on the user interface, allowing the user to visually confirm the value of each item.

[0092] Step 11:

[0093] The user can decide whether to sell or dispose of the item based on the displayed market price information, and can take further action (for example, proceed with the sale procedure) if necessary.

[0094] This detailed flow allows users to efficiently assess the value of items with minimal work and take appropriate action immediately.

[0095] Example 1

[0096] 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."

[0097] In the past, it was difficult to quickly and accurately assess the value of items, and users had to manually assess the value of each item, which required a significant amount of time and effort. Furthermore, manual assessments were inaccurate, making it difficult to obtain an accurate purchase price. To solve these problems, a system was needed that could take photos of items, automatically analyze the images, and display the purchase price.

[0098] 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.

[0099] In this invention, the server includes an imaging means for a user to capture images of multiple items, a preprocessing means for performing noise removal, image normalization, and light adjustment on the captured images, an identification means for individually identifying each item from the preprocessed images using an item detection algorithm and specifying the area, a feature extraction means for extracting character recognition, shape features, and color features as feature quantities of each identified item, a transmission means for transmitting the extracted feature quantities via the Internet, a search means for the server to search information in a database based on the feature quantities and obtain purchase price information, and a display means for receiving the obtained purchase price information and visually presenting it to the user, thereby enabling the user to quickly and accurately evaluate the value of the items.

[0100] "User" refers to the person who takes an image of an item of interest and inputs it into the system.

[0101] An "item" is an object whose value a user wishes to assess through the system.

[0102] "Photographing means" refers to a device such as a camera that a user uses to take an image of an item.

[0103] The "pre-processing means" refers to a means for performing noise removal, image normalization, and light adjustment on the captured image.

[0104] "Identification means" refers to a means for individually identifying each item and specifying its region from the preprocessed image using an item detection algorithm.

[0105] "Features" refers to information such as character recognition, shape features, and color features extracted from each identified item.

[0106] The "feature extraction means" refers to a means for extracting the feature amount of each identified item.

[0107] The "transmission means" refers to a means for transmitting the extracted feature amount to a server via the Internet.

[0108] The "search means" refers to a means for searching the database based on the feature amount received by the server and obtaining purchase price information.

[0109] "Database" refers to an information aggregation system that stores information on various items and purchase prices.

[0110] The "display means" refers to a means for visually presenting the acquired purchase price information to the user.

[0111] "Noise reduction" refers to a process for removing unnecessary noise from a captured image.

[0112] "Image normalization" refers to the process of adjusting the size and color tone of a captured image to a certain standard.

[0113] "Light adjustment" refers to the process of appropriately adjusting the brightness and contrast of a captured image.

[0114] "Item detection algorithm" refers to a computational method for detecting item regions in an image.

[0115] "Optical character recognition technology" refers to technology for extracting text information from images.

[0116] A "convolutional neural network" is a type of deep learning technology used in image analysis.

[0117] MODE FOR CARRYING OUT THE INVENTION

[0118] The present invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. This system allows users to take a photo of the item, and AI automatically displays the market price for the item using the photo. The following describes in detail the embodiments of the present invention.

[0119] System Overview

[0120] The system consists of the following main components:

[0121] 1. A means for users to take pictures

[0122] 2. Pre-processing means for noise reduction, image normalization, and light adjustment of the captured images.

[0123] 3. Identification means for individually identifying each item and identifying its area using an item detection algorithm from the preprocessed image.

[0124] 4. Feature extraction means for extracting character recognition (OCR), shape features, and color features as features of each item

[0125] 5. A means for transmitting the extracted features to a server via the Internet

[0126] 6. A search method for searching the database based on the feature values ​​received by the server and obtaining purchase price information

[0127] 7. Display means for visually presenting the acquired price information to the user

[0128] Hardware and software used

[0129] This system uses the following hardware and software:

[0130] User device: smartphone or tablet

[0131] Camera module: A camera built into a smartphone or tablet

[0132] Pre-processing software: Image processing library (e.g., OpenCV)

[0133] Object detection algorithms: YOLO (You Only Look Once) and Faster R-CNN

[0134] Character Recognition Software (OCR): Tesseract OCR

[0135] Communication protocol: HTTP or WebSocket

[0136] Server: Cloud server, database management system (e.g., MySQL (registered trademark))

[0137] Display software: User interface library (e.g., React, Vue.js)

[0138] Specific examples of explanation

[0139] Next, a specific example of use is shown.

[0140] Example: Determining the buyback price of a trading card

[0141] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0142] 2. The device receives the photo and performs preprocessing such as noise reduction and light adjustment using the OpenCV image processing library.

[0143] 3. The device uses an object detection algorithm (such as YOLO or Faster R-CNN) to identify the location of each card and extract each card individually.

[0144] 4. The terminal extracts features such as the card name and series number from each card using Tesseract OCR or similar.

[0145] 5. The device aggregates the extracted features and sends them to the server using HTTP or WebSocket.

[0146] 6. The server searches the internal database (MySQL) based on the features and obtains the market price for buying each card.

[0147] 7. The server sends the acquired market price information in JSON format to the terminal.

[0148] 8. The device displays the price information in a user interface (e.g., React or Vue.js) and provides it to the user.

[0149] Prompt Sentence Examples

[0150] "Users take photos of multiple trading cards with their smartphone camera."

[0151] "The device will remove noise from the captured image and adjust the lighting."

[0152] "The terminal uses an object detection algorithm to determine the location of each card."

[0153] The device uses Tesseract OCR to extract features and sends them to the server.

[0154] The server searches its internal database based on the features and obtains the purchase price information.

[0155] "The terminal displays information on a user interface and provides it to the user."

[0156] In this way, by using the system of the present invention, users can quickly and accurately evaluate the market price for buying an item.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1:

[0159] The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they wish to sell. The input is image data acquired through the camera, and the output is the captured image file. Specifically, the user operates the camera app to take a photo of an item (e.g., a trading card).

[0160] Step 2:

[0161] The device receives images taken by the user and performs preprocessing. The input is the captured image file, and the output is preprocessed image data. Preprocessing involves applying a noise reduction filter, normalizing the image, and adjusting the brightness. Specifically, noise reduction and brightness adjustment are performed automatically using an image processing library (e.g., OpenCV).

[0162] Step 3:

[0163] The device analyzes the preprocessed image and identifies the object using an object detection algorithm (e.g., YOLO or Faster R-CNN). The input is the preprocessed image data, and the output is the bounding box (area) of the object. Specifically, the device runs the algorithm to detect the object in the image and obtain its coordinate information.

[0164] Step 4:

[0165] The device extracts features from each identified object. The input is image data including the object's bounding box, and the output is the extracted features (e.g., character, shape, color). Feature extraction involves character recognition (OCR), shape analysis, and color feature extraction. Specifically, it uses Tesseract OCR to extract character information and executes a shape analysis algorithm.

[0166] Step 5:

[0167] The device collects the extracted features and sends them to a server via the Internet. The input is the feature data (e.g., in JSON format), and the output is the results sent to the server. Specifically, the data is sent to the server using HTTP or WebSocket protocol.

[0168] Step 6:

[0169] The server searches the database based on the received features. The input is the feature data sent from the device, and the output is the purchase price information as a search result. Specifically, it generates a database query and compares it with the internal item information.

[0170] Step 7:

[0171] The server obtains purchase price information and sends it to the terminal. The input is the purchase price information from the database, and the output is the price information (e.g., JSON format) sent to the terminal. Specifically, it formats the obtained price information and sends it to the terminal via the Internet.

[0172] Step 8:

[0173] The terminal analyzes the received price information and prepares it for display on the user interface. The input is the price information received from the server (e.g., JSON format), and the output is the visual information displayed on the user interface. Specifically, the price information is displayed in list format on the screen using React and Vue.js.

[0174] (Application example 1)

[0175] 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."

[0176] Conventional product purchase price appraisal systems have the drawback of requiring users to input product information individually, which is time-consuming and laborious. Furthermore, the lack of highly accurate product identification and feature extraction results inconsistent appraisal results. Furthermore, the user interface is not intuitive, making it difficult for users to use.

[0177] 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.

[0178] In this invention, the server includes a means for preprocessing images taken by a user and identifying the items, a means for extracting feature quantities of the items, and a means for searching a database to obtain buy price information. This allows for efficient preprocessing and identification even when a user takes photos of multiple items at once, making it possible to quickly provide accurate buy price information.

[0179] The "photography means" is a device or function for taking an image of an item that a user wishes to sell, and typically uses a camera built into a smart device.

[0180] The "pre-processing means" is a device or function that performs processes such as noise removal, image normalization, and lighting adjustment on the captured image.

[0181] "Identification means" is a device or function that uniquely identifies each item from the pre-processed image, typically using an object detection algorithm.

[0182] The "feature extraction means" is a device or function that extracts feature quantities from each identified item, and includes character recognition, shape features, color features, and the like.

[0183] The "transmission means" is a device or function for transmitting the extracted features to a server, and uses HTTP or WebSocket as a communication protocol.

[0184] The "search means" is a device or function that searches the database in the server and obtains the purchase price information of the corresponding item.

[0185] The "display means" is a device or function that displays the acquired purchase price information to the user, and provides the information visually via a user interface.

[0186] An "artificial intelligence model" is a trained algorithm or technology used for item identification or feature extraction, including YOLO and OCR.

[0187] "Smart device" is a general term for electronic devices such as mobile phones and tablet devices equipped with a camera function.

[0188] The system embodying the present invention allows a user to take an image of an item they wish to sell, and then quickly and efficiently evaluates the purchase price of the item based on the image. A specific embodiment of the system will be described below.

[0189] System Configuration

[0190] The system mainly consists of the following components:

[0191] 1. Photography Method:

[0192] It is a means for a user to take an image of an item, typically using a camera built into a smart device.

[0193] 2. Pretreatment methods:

[0194] It is a means of preprocessing the captured images such as noise removal, image normalization, lighting adjustment, etc. This is performed using the OpenCV library.

[0195] 3. Means of identification:

[0196] It is a means of identifying objects from preprocessed images and uses the YOLO (You Only Look Once) algorithm to detect objects.

[0197] 4. Feature extraction method:

[0198] This is a means of extracting features from identified items, including character recognition (OCR), shape features, color features, etc. Specifically, the Tesseract OCR engine is used.

[0199] 5. Means of transmission:

[0200] This is a means for sending extracted features to a server, and uses HTTP or WebSocket as the communication protocol.

[0201] 6. Search Methods:

[0202] This is a method of searching a database based on the feature values ​​received on the server side and obtaining the purchase price information for the corresponding item. The database can be, for example, PostgreSQL or MySQL.

[0203] 7. Display means:

[0204] This is a means of displaying the acquired purchase price information to the user, and provides the information visually via a user interface. For display, UI libraries such as React Native and Flutter (registered trademark) are used.

[0205] Hardware and Software Configuration

[0206] Hardware:

[0207] Smart devices (mobile devices with camera functions and tablet devices)

[0208] Server (Cloud server or on-premise server)

[0209] software:

[0210] OpenCV (image preprocessing)

[0211] YOLO (article identification)

[0212] Tesseract OCR engine (feature extraction)

[0213] HTTP / WebSocket (communication protocol)

[0214] Database (PostgreSQL, MySQL, etc.)

[0215] React Native or Flutter (user interface)

[0216] Specific examples

[0217] Take the example of a user selling a smartphone that they no longer need.

[0218] 1. The user launches the app and takes a picture of the smartphone they want to sell.

[0219] 2. The device receives the image and uses OpenCV to remove noise and adjust the lighting.

[0220] 3. The device uses YOLO to identify the smartphone and uses the Tesseract OCR engine to extract features such as the model name and serial number.

[0221] 4. The device sends the features to the server via the HTTP protocol.

[0222] 5. The server searches the database based on the received features and obtains the purchase price information.

[0223] 6. The server sends the acquired data to the terminal.

[0224] 7. The terminal uses React Native or Flutter to display pricing information in the user interface.

[0225] Prompt Sentence Examples

[0226] As an example of how a user might give instructions to the system, the following prompt sentences might be used:

[0227] "Please take a photo on your smartphone and let us know the purchase price."

[0228] This system allows users to quickly and accurately find out the purchase price of their items, significantly reducing the amount of time and effort required.

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] A user uses the camera on their smart device to take a picture of the item they want to sell (e.g., a smartphone, accessory, etc.). The input is the image of the item taken by the user, and the output is the captured image file.

[0232] Step 2:

[0233] The device receives the captured image and performs preprocessing. The input is the captured image file, and the output is the preprocessed image. This preprocessing includes specific operations such as noise removal, image normalization, and lighting adjustment using the OpenCV library.

[0234] Step 3:

[0235] The device analyzes the preprocessed image and identifies the object using the YOLO algorithm. The input is the preprocessed image, and the output is the object's location (bounding box). Specifically, the YOLO model identifies the object's area.

[0236] Step 4:

[0237] The device extracts features from the identified items. The input is an image with the item's location information, and the output is the extracted features (e.g., text information, color features, shape features). Specifically, the Tesseract OCR engine is used to extract the item's model name and serial number as text data.

[0238] Step 5:

[0239] The device sends the extracted features to the server using HTTP or WebSocket protocol. The input is the extracted feature data, and the output is a message that the transmission was successful. The specific operation includes encoding the features as parameters of an HTTP POST request and sending them over the network.

[0240] Step 6:

[0241] The server searches the database based on the received features and obtains the corresponding purchase price information. The input is feature data, and the output is purchase price information. Specific data processing involves using SQL queries to search for related information in the database and obtain the corresponding purchase price information.

[0242] Step 7:

[0243] The server sends the acquired purchase price information to the terminal. The input is the purchase price information, and the output is a message that transmission to the terminal was successful. Specific operations include formatting the price information as an HTTP response and sending it back over the network.

[0244] Step 8:

[0245] The terminal displays the received price information on a user interface. The input is the purchase price information received from the server, and the output is the price information visually displayed to the user. Specific operations include updating UI components using React Native or Flutter.

[0246] This series of processes allows the user to quickly find out the selling price of the item.

[0247] 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.

[0248] This invention combines an emotion engine with a system that allows users to quickly and efficiently evaluate the value of an item. Users take a photo, and the AI ​​not only automatically displays the market price for the item using that photo, but also recognizes the user's emotions and optimizes the evaluation process.

[0249] System Overview

[0250] The system consists of the following main components:

[0251] 1. A means for users to take pictures

[0252] 2. Preprocessing means for preprocessing the captured images

[0253] 3. A means of identifying each item individually from the preprocessed image.

[0254] 4. Feature extraction method to extract the features of each item

[0255] 5. A method for transmitting extracted features to the server

[0256] 6. A search method to search the database on the server and obtain purchase price information

[0257] 7. Display means for receiving the acquired price information and displaying it on the user interface

[0258] 8. Emotion engine that recognizes emotions by analyzing the user's facial expressions and voice

[0259] Program processing

[0260] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[0261] 2. The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[0262] 3. The device analyzes the preprocessed image and uses the discrimination means to identify individual items. It uses an item detection algorithm to locate each item and generate a bounding box.

[0263] 4. For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0264] 5. The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP or WebSocket).

[0265] 6. The server searches the database based on the received feature values, matches the corresponding item information in the database using the feature values ​​as a key, and obtains the purchase price information.

[0266] 7. The server sends the acquired price information to the terminal, using an appropriate protocol to ensure data reliability and communication stability.

[0267] 8. The terminal parses the price information and prepares the data for display on the user interface in a visually friendly format.

[0268] 9. The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[0269] 10. The device will adapt the user interface based on the perceived emotion. For example, it could be designed to negotiate a price or offer additional services if it determines the user is dissatisfied.

[0270] Specific examples

[0271] Below are some specific usage examples.

[0272] Example: Determining the buyback price of trading cards and taking user sentiment into account

[0273] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0274] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[0275] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[0276] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[0277] 5. The device sends the extracted features to the server.

[0278] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[0279] 7. The server sends the acquired market price information to the terminal.

[0280] 8. The terminal displays the price information on the user interface.

[0281] 9. The device captures the user's facial expressions with a camera and collects audio with a microphone, which is then analyzed by an emotion engine.

[0282] 10. If the device determines using its emotion engine that the user is not satisfied, it will make additional offers or negotiate.

[0283] This flow allows users to quickly and easily check the buying price of trading cards and take appropriate action based on their feelings. This system allows users to efficiently evaluate the value of items with minimal work and take appropriate action immediately.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] Users launch the app and use the camera function to take a photo of the item they want to sell, using the camera on their smartphone or tablet.

[0287] Step 2:

[0288] The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization, thereby improving the accuracy of image recognition.

[0289] Step 3:

[0290] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, generating a bounding box that identifies each object individually.

[0291] Step 4:

[0292] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0293] Step 5:

[0294] The device compresses the extracted features into data packets and sends them to a server over the Internet using a communication protocol (e.g., HTTP or WebSocket).

[0295] Step 6:

[0296] The server analyzes the received feature data and searches the database. Using the feature data as a key, it matches the corresponding item information in the database and obtains the purchase price information.

[0297] Step 7:

[0298] The server sends the acquired purchase price information to the terminal, using an appropriate protocol such as HTTPS to ensure data reliability and communication stability.

[0299] Step 8:

[0300] The terminal parses the received price information and prepares the data for display on the user interface in a visually friendly format.

[0301] Step 9:

[0302] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[0303] Step 10:

[0304] The device adjusts the user interface and display content based on the user's emotions recognized by the emotion engine. For example, if the device determines that the user is dissatisfied, it is designed to offer price negotiations or additional offers.

[0305] Step 11:

[0306] Users can then review the final displayed price information and any additional offers and decide whether to sell or dispose of the item. If necessary, they can proceed with the sale process within the app.

[0307] This system not only allows users to efficiently evaluate the value of an item by taking a photo and analyzing emotions once, but also allows them to receive the most appropriate service based on their emotions.

[0308] Example 2

[0309] 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."

[0310] In conventional purchase price evaluation systems, even if a user takes a picture of an item, the system does not accurately identify the item or extract its features, often resulting in an incorrect price evaluation. Furthermore, because the system does not take into account the user's feelings, it does not take appropriate action even if the user is dissatisfied. This leads to the problem of reduced user convenience and satisfaction.

[0311] 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: an imaging means for a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting feature values ​​for each identified item; a transmission means for transmitting the extracted feature values ​​to the server; a search means for the server to search for corresponding information in a database based on the feature values ​​and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition acquisition means for acquiring the user's facial expressions and voice to recognize emotions; and an interface adjustment means for adjusting the user interface based on the results of the emotion recognition. This makes it possible to accurately extract feature values ​​from images of items captured by the user, present an appropriate buy price, and adjust the interface according to the user's satisfaction.

[0312] "Photographing means" refers to a device or function that allows a user to take images of multiple items.

[0313] "Pre-processing means" refers to processing of captured images, such as noise removal, light adjustment, and resolution optimization.

[0314] "Identification means" refers to algorithms or functions that uniquely identify and locate each item in the pre-processed image.

[0315] "Feature extraction means" refers to technology or algorithms for extracting features such as character recognition, shape features, and color features of each identified item.

[0316] The "transmission means" refers to a device or communication protocol for transmitting the extracted feature quantity to a server.

[0317] "Search means" refers to a technology or system for searching for corresponding information in a database based on the features received by the server and obtaining purchase price information.

[0318] The "display means" refers to a device or interface for displaying the acquired purchase price information to the user.

[0319] "Acquisition means for emotion recognition" refers to devices or technologies for acquiring a user's facial expressions and voice and recognizing emotions.

[0320] "Interface adjustment means" refers to functions and technologies for adjusting the user interface based on the results of emotion recognition.

[0321] The present invention combines an emotion recognition engine with a system that allows users to quickly and efficiently evaluate the value of an item. Specific embodiments of this system will be described below.

[0322] System Overview

[0323] The present invention is a system that allows users to take images of multiple items using the camera on their smartphone or tablet, extracts features of the items based on the images, and displays the purchase price via a server.Furthermore, it has the feature of recognizing the user's emotions and adjusting the interface appropriately.

[0324] Hardware and software used

[0325] Image capture method: The built-in camera of a smartphone or tablet. This hardware is used to capture images of the item.

[0326] Pre-processing means: Image processing library (e.g. OpenCV). This software denoises the images, adjusts lighting, and optimizes resolution.

[0327] Identification method: Item detection algorithms (e.g., YOLO, SSD). These algorithms identify individual items from pre-processed images.

[0328] Feature extraction methods: OCR software and algorithms for extracting shape and color features. Used to extract the features of an object.

[0329] Transmission method: A communication protocol such as HTTP or WebSocket. The extracted features are sent to the server using this protocol.

[0330] Search method: Database search algorithm. The server searches for corresponding information in the database based on the features received and obtains the purchase price.

[0331] Display: The display device of a smartphone or tablet. This device is used to display the buyback price to the user.

[0332] Acquisition means for emotion recognition: Camera and microphone. These hardware capture the user's facial expressions and voice.

[0333] Emotion Recognition Engine: Machine learning algorithms (e.g., facial recognition, voice emotion identification technology) used to analyze user emotions.

[0334] Interface adjustment means: A software library for dynamically changing the user interface. The interface is adjusted based on the results of emotion recognition.

[0335] Specific examples

[0336] Below are some specific usage examples.

[0337] Example: Determining the buyback price of trading cards and taking user sentiment into account

[0338] 1. User: Arranges several trading cards from home on a table and takes a photo using the camera on their smartphone.

[0339] 2. Device: Receives the photo and uses an image processing library to perform preprocessing such as noise reduction and light adjustment.

[0340] 3. Terminal: Using an object detection algorithm, each card is located and individually cut out from the preprocessed image.

[0341] 4. Terminal: Using OCR software, extract features such as the card name and serial number from each card.

[0342] 5. Terminal: The extracted features are sent to the server using the HTTP protocol.

[0343] 6. Server: Search the database based on the features and obtain the market price for buying each card.

[0344] 7. Server: Sends the acquired market price information to the terminal using the HTTP protocol.

[0345] 8. Terminal: Display price information in a user interface.

[0346] 9. Device: Using a camera and microphone, the user's facial expressions and voice are captured and analyzed using an emotion recognition engine.

[0347] 10. Terminal: Based on the results of emotion recognition, if the user is not satisfied, the terminal will suggest additional offers or price negotiations.

[0348] Examples of prompts for generative AI models

[0349] Here are some example prompts to input to the generative AI model:

[0350] "Please describe in detail a system that takes a user's photo of a trading card, displays the market value of each card, and suggests additional offers based on the user's sentiment. Please also provide a detailed description of each processing step and its specific behavior."

[0351] This prompt allows the generative AI model to provide a detailed explanation of each processing step of the system and the specific actions that accompany it.

[0352] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0353] Step 1:

[0354] The user launches the app and uses the camera to take a photo of the item they want to sell.

[0355] Input: An image taken by a user using the camera on their smartphone or tablet.

[0356] How it works: The user launches the app, selects the camera function, points the camera at an object, and presses the shutter button.

[0357] Output: Image data of the item.

[0358] Step 2:

[0359] The device receives the captured images and performs pre-processing such as noise removal, light adjustment, and resolution optimization.

[0360] Input: Image data of photographed item.

[0361] What it does: It uses an image processing library (e.g. OpenCV) to remove noise from the image, adjust color and brightness, and optionally resize and optimize the resolution of the image.

[0362] Output: Preprocessed image data.

[0363] Step 3:

[0364] The terminal analyzes the pre-processed images and identifies individual items using the identification means.

[0365] Input: Preprocessed image data.

[0366] How it works: Uses an object detection algorithm (e.g., YOLO or SSD) to locate the object and generate a bounding box.

[0367] Output: Bounding box coordinate data for each object.

[0368] Step 4:

[0369] The terminal extracts features for each identified item using a feature extraction algorithm.

[0370] Input: Bounding box coordinate data and preprocessed image data for each object.

[0371] How it works: It uses OCR (Optical Character Recognition) software to extract text information from an object, as well as analyze features such as color and shape, and saves them as a digital signature.

[0372] Output: Feature data for each item.

[0373] Step 5:

[0374] The terminal compresses the extracted features into a data packet and transmits it to the server.

[0375] Input: Feature data for each item.

[0376] How it works: The feature data is efficiently compressed using a compression algorithm and sent to a server using the HTTP protocol.

[0377] Output: A data packet containing feature data.

[0378] Step 6:

[0379] The server searches the database based on the received features.

[0380] Input: A data packet containing the received feature data.

[0381] Operation: The server analyzes the features and uses the index to search for the corresponding item data in the database. The search result is the purchase price information.

[0382] Output: Purchase price information data for each item.

[0383] Step 7:

[0384] The server transmits the acquired price information to the terminal.

[0385] Input: Purchase price information data for each item.

[0386] How it works: The server reassembles the acquired price information into data packets and sends them to the device using the HTTP protocol, retransmitting and acknowledging them as necessary to ensure communication stability.

[0387] Output: A data packet containing the buy price information.

[0388] Step 8:

[0389] The terminal parses the price information and prepares the data for display on the user interface.

[0390] Input: A data packet containing the buy price information for each item.

[0391] What it does: Formats the retrieved pricing information based on your user interface design and displays it in a visually easy-to-understand format (e.g., graph or comparison table).

[0392] Output: Price display data on the user interface.

[0393] Step 9:

[0394] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed using an emotion engine.

[0395] Input: User's facial expression and voice data.

[0396] How it works: It acquires video data from the camera and audio data from the microphone, and uses emotion analysis algorithms (e.g., facial recognition and voice emotion identification technology) to recognize the user's emotions.

[0397] Output: User's emotional state data.

[0398] Step 10:

[0399] The device adjusts the user interface based on the recognized emotion.

[0400] Input: User emotional state data.

[0401] How it works: If the system determines that the user is dissatisfied, it automatically displays additional offers and price negotiation suggestions, and customizes interface elements and messages based on emotion recognition results.

[0402] Output: Adjusted user interface display data.

[0403] (Application example 2)

[0404] 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."

[0405] In conventional product value evaluation systems, when a user evaluates the value of an item, the system simply automatically displays the market price for the item, and does not take appropriate action that takes into account the user's feelings or satisfaction. As a result, if the user is dissatisfied with the system's display or wishes to negotiate further, the system is unable to respond, resulting in a poor user experience.

[0406] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an imaging means for allowing a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting features of each identified item; a transmission means for transmitting the extracted features to the server; a search means for the server to search for corresponding information in a database based on the features and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition means for capturing the user's facial expressions with a camera and collecting voice with a microphone to recognize emotions; and an adjustment means for adjusting the interface for the user based on the emotion recognized by the emotion recognition means. This makes it possible to automatically make additional offers or propose price negotiations even if the user is not satisfied with the offered price, thereby improving the user experience.

[0407] "Photographing means" refers to a device or function that allows a user to photograph an image of an item.

[0408] The "pre-processing means" is a function for performing pre-processing such as noise removal and light adjustment on the captured image.

[0409] "Identification means" refers to algorithms or functions that identify individual items from pre-processed images.

[0410] The "feature extraction means" is a function for extracting information such as character recognition, shape features, and color features from the identified article.

[0411] The "transmission means" is a communication means for transmitting the extracted feature amount to the server.

[0412] The "search means" is a function for searching the database based on the feature amount received by the server and acquiring corresponding information.

[0413] The "display means" is an interface or device for visually notifying the user of the acquired purchase price information.

[0414] The "emotion recognition means" is a function for recognizing emotions by analyzing the user's facial expressions and voice.

[0415] The "adjustment means" is a function for adjusting the user interface based on the emotion recognized by the emotion recognition means.

[0416] The present invention provides a system that allows users to quickly and efficiently evaluate the value of items, incorporating an emotion engine. Using photos taken by the user, AI automatically displays the market price for the item, and is also able to recognize the user's emotions and optimize the evaluation process. An embodiment of this system is shown below.

[0417] System configuration

[0418] 1. Photography: The user uses the camera on their smartphone or tablet to take a picture of the item.

[0419] 2. Pre-processing: The captured images are pre-processed using OpenCV to remove noise, adjust lighting, and optimize resolution.

[0420] 3. Identification: Use the TENSORFLOW® Object Detection API to identify each item individually from the pre-processed image.

[0421] 4. Feature extraction: Using Tesseract OCR or custom CNN, features such as character recognition, shape features, and color features of the identified items are extracted.

[0422] 5. Transmission means: The extracted features are compressed into data packets and transmitted to the server using a communication protocol (e.g., HTTP / HTTPS).

[0423] 6. Search method: The server uses MongoDB to search for corresponding information in the database based on the received features and obtains the purchase price information.

[0424] 7. Display means: The acquired purchase price information is sent to the terminal and visually displayed on the user interface.

[0425] 8. Emotion recognition means: Using Microsoft® Azure® Face API or Google® Cloud Speech-to-Text, the user's facial expressions and voice are collected and emotions are recognized.

[0426] 9. Adjustment: Dynamically adjust the user interface based on emotions recognized by the emotion engine, such as negotiating prices or presenting additional offers.

[0427] Specific examples

[0428] Trading card valuation and emotional response

[0429] A user arranges multiple trading cards on a table at home and takes a photo with their smartphone camera. After taking the photo, the device preprocesses the image and identifies each card using an item detection algorithm. Next, Tesseract OCR extracts features such as the card name and series number, and sends the data packet to the server. The server uses MongoDB to search for the repurchase price information for the corresponding card and sends the information to the device. The device then displays the obtained price information on its user interface. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, and recognizes their emotions using the Microsoft Azure Face API or Google Cloud Speech-to-Text. If the recognized emotion is determined to be "unhappy," the system immediately presents additional offers or price negotiations.

[0430] Prompt Sentence Examples

[0431] Example prompt for Tesseract OCR:

[0432] Recognize text from image: "Figure name", "Manufacturer", "Series number"

[0433] In this way, the system of the present invention can quickly and efficiently evaluate the value of an item to the user and provide appropriate actions that take emotions into consideration.

[0434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0435] Step 1:

[0436] The user launches the app and takes a photo of the item using the smartphone's camera. The image is then input into the device. The user follows the app's instructions to take a photo of the item under appropriate lighting.

[0437] Step 2:

[0438] The device receives the captured image and uses OpenCV to perform preprocessing such as noise removal, light adjustment, and resolution optimization. The input of the preprocessing is the captured image, and the output is the preprocessed image. Specifically, image filtering and contrast adjustment are performed.

[0439] Step 3:

[0440] The device analyzes the preprocessed image using the TensorFlow Object Detection API and uses a discriminator to identify individual objects. The input is the preprocessed image, and the output is a bounding box indicating the location of the object. The algorithm detects features in the image and identifies the area of ​​each object.

[0441] Step 4:

[0442] For each identified item, the device uses Tesseract OCR and a custom CNN to extract features such as character recognition, shape features, and color features. The input is the image region for each item, and the output is the extracted features. OCR reads the character information, and CNN analyzes the shape and color patterns.

[0443] Step 5:

[0444] The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP / HTTPS). The input is the features, and the output is the data packets sent to the server. This includes data compression and encryption.

[0445] Step 6:

[0446] The server uses MongoDB to search the database based on the received features and obtain the corresponding purchase price information. The input is the received features and the output is the obtained purchase price information. A database search algorithm is executed to extract the optimal price information.

[0447] Step 7:

[0448] The server sends the acquired purchase price information to the terminal. The input is the price information, and the output is the data sent to the terminal. Again, a communication protocol (e.g., HTTP / HTTPS) is used.

[0449] Step 8:

[0450] The terminal parses the received price information and prepares the data for visual display in the user interface. The input is the price information and the output is the information displayed to the user. It displays the information in a visually easy-to-understand graphical format.

[0451] Step 9:

[0452] The device uses a camera and microphone to collect the user's facial expressions and voice, and recognizes emotions using the Microsoft Azure Face API and Google Cloud Speech-to-Text. The input is a photo of the user's facial expression and voice, and the output is analyzed emotional information. The emotion recognition algorithm analyzes the data and identifies the user's emotion.

[0453] Step 10:

[0454] The device executes an adjustment method to adjust the user interface based on the recognized emotion. The input is emotion information, and the output is an adjusted user interface. Specifically, if satisfaction is low, price negotiation or additional offers are presented.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] [Second embodiment]

[0459] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0470] 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."

[0471] This invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. Specifically, this describes a system in which a user takes a photo and an AI automatically displays the market price for the item using the photo.

[0472] System Overview

[0473] The system consists of the following main components:

[0474] 1. A means for users to take pictures

[0475] 2. Preprocessing means for preprocessing the captured images

[0476] 3. A means of identifying each item individually from the preprocessed image.

[0477] 4. Feature extraction method to extract the features of each item

[0478] 5. A method for transmitting extracted features to the server

[0479] 6. A search method to search the database on the server and obtain purchase price information

[0480] 7. Display means for receiving the acquired price information and displaying it on the user interface

[0481] Program processing

[0482] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[0483] 2. The device receives the captured image and performs preprocessing, which includes noise reduction, image normalization, and light adjustment.

[0484] 3. The device analyzes the preprocessed image and uses a classification method to identify individual objects. This process uses an object detection algorithm (such as YOLO or Faster R-CNN), identifies the object's bounding box, and separates each object.

[0485] 4. The terminal extracts features from the identified items. Feature extraction methods include character recognition (OCR), shape features, and color features of the items.

[0486] 5. The device sends the extracted features to the server using a communication protocol over the Internet (e.g., HTTP or WebSocket).

[0487] 6. The server searches the database based on the received feature values. The search means uses the feature values ​​as a key to match the product information in the database and obtains the corresponding purchase price information.

[0488] 7. The server sends the acquired price information to the device, allowing the user to check the value of each item through the app.

[0489] 8. The terminal analyzes the received price information and prepares it for display on a user interface, including an interface that visually presents the price information to the user in a grid or list format.

[0490] Specific examples

[0491] Below are some specific usage examples.

[0492] Example: Determining the buyback price of a trading card

[0493] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0494] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[0495] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[0496] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[0497] 5. The device aggregates the extracted features and sends them to the server.

[0498] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[0499] 7. The server sends the acquired market price information to the terminal.

[0500] 8. The terminal displays the price information on the user interface and provides it to the user.

[0501] This flow allows users to quickly and easily check the buying price of trading cards and decide whether to sell them.

[0502] This system allows users to efficiently evaluate the value of multiple items at once, significantly reducing time and effort.

[0503] The processing flow will be explained below.

[0504] Step 1:

[0505] Users launch the app and use the camera to take a photo of the item they want to sell. They can also take a photo of multiple items at once.

[0506] Step 2:

[0507] The device receives the captured image and performs pre-processing such as noise removal, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[0508] Step 3:

[0509] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, which generates a bounding box to uniquely identify each object.

[0510] Step 4:

[0511] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0512] Step 5:

[0513] The device compresses the extracted features into data packets and transmits them to a server over the Internet, using common communication protocols (e.g., HTTP or WebSocket) to ensure reliable and fast transmission.

[0514] Step 6:

[0515] The server analyzes the received feature data and searches the database. This search is performed by using the feature as a key to match the corresponding product information in the database.

[0516] Step 7:

[0517] The server retrieves market price information for the relevant items from its database and compiles it into a data packet, which identifies how much each item is worth.

[0518] Step 8:

[0519] The server sends the acquired market price information to the terminal, using a protocol (e.g., HTTPS) to ensure data integrity and communication stability.

[0520] Step 9:

[0521] The terminal analyzes the received market price information and prepares the data for display on the user interface, where the data is arranged in a format that is intuitively understandable to the user (for example, a grid or list format).

[0522] Step 10:

[0523] The terminal displays market price information on the user interface, allowing the user to visually confirm the value of each item.

[0524] Step 11:

[0525] The user can decide whether to sell or dispose of the item based on the displayed market price information, and can take further action (for example, proceed with the sale procedure) if necessary.

[0526] This detailed flow allows users to efficiently assess the value of items with minimal work and take appropriate action immediately.

[0527] Example 1

[0528] 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."

[0529] In the past, it was difficult to quickly and accurately assess the value of items, and users had to manually assess the value of each item, which required a significant amount of time and effort. Furthermore, manual assessments were inaccurate, making it difficult to obtain an accurate purchase price. To solve these problems, a system was needed that could take photos of items, automatically analyze the images, and display the purchase price.

[0530] 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.

[0531] In this invention, the server includes an imaging means for a user to capture images of multiple items, a preprocessing means for performing noise removal, image normalization, and light adjustment on the captured images, an identification means for individually identifying each item from the preprocessed images using an item detection algorithm and specifying the area, a feature extraction means for extracting character recognition, shape features, and color features as feature quantities of each identified item, a transmission means for transmitting the extracted feature quantities via the Internet, a search means for the server to search information in a database based on the feature quantities and obtain purchase price information, and a display means for receiving the obtained purchase price information and visually presenting it to the user, thereby enabling the user to quickly and accurately evaluate the value of the items.

[0532] "User" refers to the person who takes an image of an item of interest and inputs it into the system.

[0533] An "item" is an object whose value a user wishes to assess through the system.

[0534] "Photographing means" refers to a device such as a camera that a user uses to take an image of an item.

[0535] The "pre-processing means" refers to a means for performing noise removal, image normalization, and light adjustment on the captured image.

[0536] "Identification means" refers to a means for individually identifying each item and specifying its region from the preprocessed image using an item detection algorithm.

[0537] "Features" refers to information such as character recognition, shape features, and color features extracted from each identified item.

[0538] The "feature extraction means" refers to a means for extracting the feature amount of each identified item.

[0539] The "transmission means" refers to a means for transmitting the extracted feature amount to a server via the Internet.

[0540] The "search means" refers to a means for searching the database based on the feature amount received by the server and obtaining purchase price information.

[0541] "Database" refers to an information aggregation system that stores information on various items and purchase prices.

[0542] The "display means" refers to a means for visually presenting the acquired purchase price information to the user.

[0543] "Noise reduction" refers to a process for removing unnecessary noise from a captured image.

[0544] "Image normalization" refers to the process of adjusting the size and color tone of a captured image to a certain standard.

[0545] "Light adjustment" refers to the process of appropriately adjusting the brightness and contrast of a captured image.

[0546] "Item detection algorithm" refers to a computational method for detecting item regions in an image.

[0547] "Optical character recognition technology" refers to technology for extracting text information from images.

[0548] A "convolutional neural network" is a type of deep learning technology used in image analysis.

[0549] MODE FOR CARRYING OUT THE INVENTION

[0550] The present invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. This system allows users to take a photo of the item, and AI automatically displays the market price for the item using the photo. The following describes in detail the embodiments of the present invention.

[0551] System Overview

[0552] The system consists of the following main components:

[0553] 1. A means for users to take pictures

[0554] 2. Pre-processing means for noise reduction, image normalization, and light adjustment of the captured images.

[0555] 3. Identification means for individually identifying each item and identifying its area using an item detection algorithm from the preprocessed image.

[0556] 4. Feature extraction means for extracting character recognition (OCR), shape features, and color features as features of each item

[0557] 5. A means for transmitting the extracted features to a server via the Internet

[0558] 6. A search method for searching the database based on the feature values ​​received by the server and obtaining purchase price information

[0559] 7. Display means for visually presenting the acquired price information to the user

[0560] Hardware and software used

[0561] This system uses the following hardware and software:

[0562] User device: smartphone or tablet

[0563] Camera module: A camera built into a smartphone or tablet

[0564] Pre-processing software: Image processing library (e.g., OpenCV)

[0565] Object detection algorithms: YOLO (You Only Look Once) and Faster R-CNN

[0566] Character Recognition Software (OCR): Tesseract OCR

[0567] Communication protocol: HTTP or WebSocket

[0568] Server: Cloud server, database management system (e.g. MySQL)

[0569] Display software: User interface library (e.g., React, Vue.js)

[0570] Specific examples of explanation

[0571] Next, a specific example of use is shown.

[0572] Example: Determining the buyback price of a trading card

[0573] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0574] 2. The device receives the photo and performs preprocessing such as noise reduction and light adjustment using the OpenCV image processing library.

[0575] 3. The device uses an object detection algorithm (such as YOLO or Faster R-CNN) to identify the location of each card and extract each card individually.

[0576] 4. The terminal extracts features such as the card name and series number from each card using Tesseract OCR or similar.

[0577] 5. The device aggregates the extracted features and sends them to the server using HTTP or WebSocket.

[0578] 6. The server searches the internal database (MySQL) based on the features and obtains the market price for buying each card.

[0579] 7. The server sends the acquired market price information in JSON format to the terminal.

[0580] 8. The device displays the price information in a user interface (e.g., React or Vue.js) and provides it to the user.

[0581] Prompt Sentence Examples

[0582] "Users take photos of multiple trading cards with their smartphone camera."

[0583] "The device will remove noise from the captured image and adjust the lighting."

[0584] "The terminal uses an object detection algorithm to determine the location of each card."

[0585] The device uses Tesseract OCR to extract features and sends them to the server.

[0586] The server searches its internal database based on the features and obtains the purchase price information.

[0587] "The terminal displays information on a user interface and provides it to the user."

[0588] In this way, by using the system of the present invention, users can quickly and accurately evaluate the market price for buying an item.

[0589] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0590] Step 1:

[0591] The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they wish to sell. The input is image data acquired through the camera, and the output is the captured image file. Specifically, the user operates the camera app to take a photo of an item (e.g., a trading card).

[0592] Step 2:

[0593] The device receives images taken by the user and performs preprocessing. The input is the captured image file, and the output is preprocessed image data. Preprocessing involves applying a noise reduction filter, normalizing the image, and adjusting the brightness. Specifically, noise reduction and brightness adjustment are performed automatically using an image processing library (e.g., OpenCV).

[0594] Step 3:

[0595] The device analyzes the preprocessed image and identifies the object using an object detection algorithm (e.g., YOLO or Faster R-CNN). The input is the preprocessed image data, and the output is the bounding box (area) of the object. Specifically, the device runs the algorithm to detect the object in the image and obtain its coordinate information.

[0596] Step 4:

[0597] The device extracts features from each identified object. The input is image data including the object's bounding box, and the output is the extracted features (e.g., character, shape, color). Feature extraction involves character recognition (OCR), shape analysis, and color feature extraction. Specifically, it uses Tesseract OCR to extract character information and executes a shape analysis algorithm.

[0598] Step 5:

[0599] The device collects the extracted features and sends them to a server via the Internet. The input is the feature data (e.g., in JSON format), and the output is the results sent to the server. Specifically, the data is sent to the server using HTTP or WebSocket protocol.

[0600] Step 6:

[0601] The server searches the database based on the received features. The input is the feature data sent from the device, and the output is the purchase price information as a search result. Specifically, it generates a database query and compares it with the internal item information.

[0602] Step 7:

[0603] The server obtains purchase price information and sends it to the terminal. The input is the purchase price information from the database, and the output is the price information (e.g., JSON format) sent to the terminal. Specifically, it formats the obtained price information and sends it to the terminal via the Internet.

[0604] Step 8:

[0605] The terminal analyzes the received price information and prepares it for display on the user interface. The input is the price information received from the server (e.g., JSON format), and the output is the visual information displayed on the user interface. Specifically, the price information is displayed in list format on the screen using React and Vue.js.

[0606] (Application example 1)

[0607] 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."

[0608] Conventional product purchase price appraisal systems have the drawback of requiring users to input product information individually, which is time-consuming and laborious. Furthermore, the lack of highly accurate product identification and feature extraction results inconsistent appraisal results. Furthermore, the user interface is not intuitive, making it difficult for users to use.

[0609] 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.

[0610] In this invention, the server includes a means for preprocessing images taken by a user and identifying the items, a means for extracting feature quantities of the items, and a means for searching a database to obtain buy price information. This allows for efficient preprocessing and identification even when a user takes photos of multiple items at once, making it possible to quickly provide accurate buy price information.

[0611] The "photography means" is a device or function for taking an image of an item that a user wishes to sell, and typically uses a camera built into a smart device.

[0612] The "pre-processing means" is a device or function that performs processes such as noise removal, image normalization, and lighting adjustment on the captured image.

[0613] "Identification means" is a device or function that uniquely identifies each item from the pre-processed image, typically using an object detection algorithm.

[0614] The "feature extraction means" is a device or function that extracts feature quantities from each identified item, and includes character recognition, shape features, color features, and the like.

[0615] The "transmission means" is a device or function for transmitting the extracted features to a server, and uses HTTP or WebSocket as a communication protocol.

[0616] The "search means" is a device or function that searches the database in the server and obtains the purchase price information of the corresponding item.

[0617] The "display means" is a device or function that displays the acquired purchase price information to the user, and provides the information visually via a user interface.

[0618] An "artificial intelligence model" is a trained algorithm or technology used for item identification or feature extraction, including YOLO and OCR.

[0619] "Smart device" is a general term for electronic devices such as mobile phones and tablet devices equipped with a camera function.

[0620] The system embodying the present invention allows a user to take an image of an item they wish to sell, and then quickly and efficiently evaluates the purchase price of the item based on the image. A specific embodiment of the system will be described below.

[0621] System Configuration

[0622] The system mainly consists of the following components:

[0623] 1. Photography Method:

[0624] It is a means for a user to take an image of an item, typically using a camera built into a smart device.

[0625] 2. Pretreatment methods:

[0626] It is a means of preprocessing the captured images such as noise removal, image normalization, lighting adjustment, etc. This is performed using the OpenCV library.

[0627] 3. Means of identification:

[0628] It is a means of identifying objects from preprocessed images and uses the YOLO (You Only Look Once) algorithm to detect objects.

[0629] 4. Feature extraction method:

[0630] This is a means of extracting features from identified items, including character recognition (OCR), shape features, color features, etc. Specifically, the Tesseract OCR engine is used.

[0631] 5. Means of transmission:

[0632] This is a means for sending extracted features to a server, and uses HTTP or WebSocket as the communication protocol.

[0633] 6. Search Methods:

[0634] This is a method of searching a database based on the feature values ​​received on the server side and obtaining the purchase price information for the corresponding item. The database can be, for example, PostgreSQL or MySQL.

[0635] 7. Display means:

[0636] This is a means of displaying the acquired purchase price information to the user, and provides the information visually through a user interface, using UI libraries such as React Native and Flutter.

[0637] Hardware and Software Configuration

[0638] Hardware:

[0639] Smart devices (mobile devices with camera functions and tablet devices)

[0640] Server (Cloud server or on-premise server)

[0641] software:

[0642] OpenCV (image preprocessing)

[0643] YOLO (article identification)

[0644] Tesseract OCR engine (feature extraction)

[0645] HTTP / WebSocket (communication protocol)

[0646] Database (PostgreSQL, MySQL, etc.)

[0647] React Native or Flutter (user interface)

[0648] Specific examples

[0649] Take the example of a user selling a smartphone that they no longer need.

[0650] 1. The user launches the app and takes a picture of the smartphone they want to sell.

[0651] 2. The device receives the image and uses OpenCV to remove noise and adjust the lighting.

[0652] 3. The device uses YOLO to identify the smartphone and uses the Tesseract OCR engine to extract features such as the model name and serial number.

[0653] 4. The device sends the features to the server via the HTTP protocol.

[0654] 5. The server searches the database based on the received features and obtains the purchase price information.

[0655] 6. The server sends the acquired data to the terminal.

[0656] 7. The terminal uses React Native or Flutter to display pricing information in the user interface.

[0657] Prompt Sentence Examples

[0658] As an example of how a user might give instructions to the system, the following prompt sentences might be used:

[0659] "Please take a photo on your smartphone and let us know the purchase price."

[0660] This system allows users to quickly and accurately find out the purchase price of their items, significantly reducing the amount of time and effort required.

[0661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0662] Step 1:

[0663] A user uses the camera on their smart device to take a picture of the item they want to sell (e.g., a smartphone, accessory, etc.). The input is the image of the item taken by the user, and the output is the captured image file.

[0664] Step 2:

[0665] The device receives the captured image and performs preprocessing. The input is the captured image file, and the output is the preprocessed image. This preprocessing includes specific operations such as noise removal, image normalization, and lighting adjustment using the OpenCV library.

[0666] Step 3:

[0667] The device analyzes the preprocessed image and identifies the object using the YOLO algorithm. The input is the preprocessed image, and the output is the object's location (bounding box). Specifically, the YOLO model identifies the object's area.

[0668] Step 4:

[0669] The device extracts features from the identified items. The input is an image with the item's location information, and the output is the extracted features (e.g., text information, color features, shape features). Specifically, the Tesseract OCR engine is used to extract the item's model name and serial number as text data.

[0670] Step 5:

[0671] The device sends the extracted features to the server using HTTP or WebSocket protocol. The input is the extracted feature data, and the output is a message that the transmission was successful. The specific operation includes encoding the features as parameters of an HTTP POST request and sending them over the network.

[0672] Step 6:

[0673] The server searches the database based on the received features and obtains the corresponding purchase price information. The input is feature data, and the output is purchase price information. Specific data processing involves using SQL queries to search for related information in the database and obtain the corresponding purchase price information.

[0674] Step 7:

[0675] The server sends the acquired purchase price information to the terminal. The input is the purchase price information, and the output is a message that transmission to the terminal was successful. Specific operations include formatting the price information as an HTTP response and sending it back over the network.

[0676] Step 8:

[0677] The terminal displays the received price information on a user interface. The input is the purchase price information received from the server, and the output is the price information visually displayed to the user. Specific operations include updating UI components using React Native or Flutter.

[0678] This series of processes allows the user to quickly find out the selling price of the item.

[0679] 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.

[0680] This invention combines an emotion engine with a system that allows users to quickly and efficiently evaluate the value of an item. Users take a photo, and the AI ​​not only automatically displays the market price for the item using that photo, but also recognizes the user's emotions and optimizes the evaluation process.

[0681] System Overview

[0682] The system consists of the following main components:

[0683] 1. A means for users to take pictures

[0684] 2. Preprocessing means for preprocessing the captured images

[0685] 3. A means of identifying each item individually from the preprocessed image.

[0686] 4. Feature extraction method to extract the features of each item

[0687] 5. A method for transmitting extracted features to the server

[0688] 6. A search method to search the database on the server and obtain purchase price information

[0689] 7. Display means for receiving the acquired price information and displaying it on the user interface

[0690] 8. Emotion engine that recognizes emotions by analyzing the user's facial expressions and voice

[0691] Program processing

[0692] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[0693] 2. The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[0694] 3. The device analyzes the preprocessed image and uses the discrimination means to identify individual items. It uses an item detection algorithm to locate each item and generate a bounding box.

[0695] 4. For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0696] 5. The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP or WebSocket).

[0697] 6. The server searches the database based on the received feature values, matches the corresponding item information in the database using the feature values ​​as a key, and obtains the purchase price information.

[0698] 7. The server sends the acquired price information to the terminal, using an appropriate protocol to ensure data reliability and communication stability.

[0699] 8. The terminal parses the price information and prepares the data for display on the user interface in a visually friendly format.

[0700] 9. The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[0701] 10. The device will adapt the user interface based on the perceived emotion. For example, it could be designed to negotiate a price or offer additional services if it determines the user is dissatisfied.

[0702] Specific examples

[0703] Below are some specific usage examples.

[0704] Example: Determining the buyback price of trading cards and taking user sentiment into account

[0705] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0706] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[0707] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[0708] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[0709] 5. The device sends the extracted features to the server.

[0710] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[0711] 7. The server sends the acquired market price information to the terminal.

[0712] 8. The terminal displays the price information on the user interface.

[0713] 9. The device captures the user's facial expressions with a camera and collects audio with a microphone, which is then analyzed by an emotion engine.

[0714] 10. If the device determines using its emotion engine that the user is not satisfied, it will make additional offers or negotiate.

[0715] This flow allows users to quickly and easily check the buying price of trading cards and take appropriate action based on their feelings. This system allows users to efficiently evaluate the value of items with minimal work and take appropriate action immediately.

[0716] The processing flow will be explained below.

[0717] Step 1:

[0718] Users launch the app and use the camera function to take a photo of the item they want to sell, using the camera on their smartphone or tablet.

[0719] Step 2:

[0720] The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization, thereby improving the accuracy of image recognition.

[0721] Step 3:

[0722] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, generating a bounding box that identifies each object individually.

[0723] Step 4:

[0724] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0725] Step 5:

[0726] The device compresses the extracted features into data packets and sends them to a server over the Internet using a communication protocol (e.g., HTTP or WebSocket).

[0727] Step 6:

[0728] The server analyzes the received feature data and searches the database. Using the feature data as a key, it matches the corresponding item information in the database and obtains the purchase price information.

[0729] Step 7:

[0730] The server sends the acquired purchase price information to the terminal, using an appropriate protocol such as HTTPS to ensure data reliability and communication stability.

[0731] Step 8:

[0732] The terminal parses the received price information and prepares the data for display on the user interface in a visually friendly format.

[0733] Step 9:

[0734] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[0735] Step 10:

[0736] The device adjusts the user interface and display content based on the user's emotions recognized by the emotion engine. For example, if the device determines that the user is dissatisfied, it is designed to offer price negotiations or additional offers.

[0737] Step 11:

[0738] Users can then review the final displayed price information and any additional offers and decide whether to sell or dispose of the item. If necessary, they can proceed with the sale process within the app.

[0739] This system not only allows users to efficiently evaluate the value of an item by taking a photo and analyzing emotions once, but also allows them to receive the most appropriate service based on their emotions.

[0740] Example 2

[0741] 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."

[0742] In conventional purchase price evaluation systems, even if a user takes a picture of an item, the system does not accurately identify the item or extract its features, often resulting in an incorrect price evaluation. Furthermore, because the system does not take into account the user's feelings, it does not take appropriate action even if the user is dissatisfied. This leads to the problem of reduced user convenience and satisfaction.

[0743] 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: an imaging means for a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting feature values ​​for each identified item; a transmission means for transmitting the extracted feature values ​​to the server; a search means for the server to search for corresponding information in a database based on the feature values ​​and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition acquisition means for acquiring the user's facial expressions and voice to recognize emotions; and an interface adjustment means for adjusting the user interface based on the results of the emotion recognition. This makes it possible to accurately extract feature values ​​from images of items captured by the user, present an appropriate buy price, and adjust the interface according to the user's satisfaction.

[0744] "Photographing means" refers to a device or function that allows a user to take images of multiple items.

[0745] "Pre-processing means" refers to processing of captured images, such as noise removal, light adjustment, and resolution optimization.

[0746] "Identification means" refers to algorithms or functions that uniquely identify and locate each item in the pre-processed image.

[0747] "Feature extraction means" refers to technology or algorithms for extracting features such as character recognition, shape features, and color features of each identified item.

[0748] The "transmission means" refers to a device or communication protocol for transmitting the extracted feature quantity to a server.

[0749] "Search means" refers to a technology or system for searching for corresponding information in a database based on the features received by the server and obtaining purchase price information.

[0750] The "display means" refers to a device or interface for displaying the acquired purchase price information to the user.

[0751] "Acquisition means for emotion recognition" refers to devices or technologies for acquiring a user's facial expressions and voice and recognizing emotions.

[0752] "Interface adjustment means" refers to functions and technologies for adjusting the user interface based on the results of emotion recognition.

[0753] The present invention combines an emotion recognition engine with a system that allows users to quickly and efficiently evaluate the value of an item. Specific embodiments of this system will be described below.

[0754] System Overview

[0755] The present invention is a system that allows users to take images of multiple items using the camera on their smartphone or tablet, extracts features of the items based on the images, and displays the purchase price via a server.Furthermore, it has the feature of recognizing the user's emotions and adjusting the interface appropriately.

[0756] Hardware and software used

[0757] Image capture method: The built-in camera of a smartphone or tablet. This hardware is used to capture images of the item.

[0758] Pre-processing means: Image processing library (e.g. OpenCV). This software denoises the images, adjusts lighting, and optimizes resolution.

[0759] Identification method: Item detection algorithms (e.g., YOLO, SSD). These algorithms identify individual items from pre-processed images.

[0760] Feature extraction methods: OCR software and algorithms for extracting shape and color features. Used to extract the features of an object.

[0761] Transmission method: A communication protocol such as HTTP or WebSocket. The extracted features are sent to the server using this protocol.

[0762] Search method: Database search algorithm. The server searches for corresponding information in the database based on the features received and obtains the purchase price.

[0763] Display: The display device of a smartphone or tablet. This device is used to display the buyback price to the user.

[0764] Acquisition means for emotion recognition: Camera and microphone. These hardware capture the user's facial expressions and voice.

[0765] Emotion Recognition Engine: Machine learning algorithms (e.g., facial recognition, voice emotion identification technology) used to analyze user emotions.

[0766] Interface adjustment means: A software library for dynamically changing the user interface. The interface is adjusted based on the results of emotion recognition.

[0767] Specific examples

[0768] Below are some specific usage examples.

[0769] Example: Determining the buyback price of trading cards and taking user sentiment into account

[0770] 1. User: Arranges several trading cards from home on a table and takes a photo using the camera on their smartphone.

[0771] 2. Device: Receives the photo and uses an image processing library to perform preprocessing such as noise reduction and light adjustment.

[0772] 3. Terminal: Using an object detection algorithm, each card is located and individually cut out from the preprocessed image.

[0773] 4. Terminal: Using OCR software, extract features such as the card name and serial number from each card.

[0774] 5. Terminal: The extracted features are sent to the server using the HTTP protocol.

[0775] 6. Server: Search the database based on the features and obtain the market price for buying each card.

[0776] 7. Server: Sends the acquired market price information to the terminal using the HTTP protocol.

[0777] 8. Terminal: Display price information in a user interface.

[0778] 9. Device: Using a camera and microphone, the user's facial expressions and voice are captured and analyzed using an emotion recognition engine.

[0779] 10. Terminal: Based on the results of emotion recognition, if the user is not satisfied, the terminal will suggest additional offers or price negotiations.

[0780] Examples of prompts for generative AI models

[0781] Here are some example prompts to input to the generative AI model:

[0782] "Please describe in detail a system that takes a user's photo of a trading card, displays the market value of each card, and suggests additional offers based on the user's sentiment. Please also provide a detailed description of each processing step and its specific behavior."

[0783] This prompt allows the generative AI model to provide a detailed explanation of each processing step of the system and the specific actions that accompany it.

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

[0785] Step 1:

[0786] The user launches the app and uses the camera to take a photo of the item they want to sell.

[0787] Input: An image taken by a user using the camera on their smartphone or tablet.

[0788] How it works: The user launches the app, selects the camera function, points the camera at an object, and presses the shutter button.

[0789] Output: Image data of the item.

[0790] Step 2:

[0791] The device receives the captured images and performs pre-processing such as noise removal, light adjustment, and resolution optimization.

[0792] Input: Image data of photographed item.

[0793] What it does: It uses an image processing library (e.g. OpenCV) to remove noise from the image, adjust color and brightness, and optionally resize and optimize the resolution of the image.

[0794] Output: Preprocessed image data.

[0795] Step 3:

[0796] The terminal analyzes the pre-processed images and identifies individual items using the identification means.

[0797] Input: Preprocessed image data.

[0798] How it works: Uses an object detection algorithm (e.g., YOLO or SSD) to locate the object and generate a bounding box.

[0799] Output: Bounding box coordinate data for each object.

[0800] Step 4:

[0801] The terminal extracts features for each identified item using a feature extraction algorithm.

[0802] Input: Bounding box coordinate data and preprocessed image data for each object.

[0803] How it works: It uses OCR (Optical Character Recognition) software to extract text information from an object, as well as analyze features such as color and shape, and saves them as a digital signature.

[0804] Output: Feature data for each item.

[0805] Step 5:

[0806] The terminal compresses the extracted features into a data packet and transmits it to the server.

[0807] Input: Feature data for each item.

[0808] How it works: The feature data is efficiently compressed using a compression algorithm and sent to a server using the HTTP protocol.

[0809] Output: A data packet containing feature data.

[0810] Step 6:

[0811] The server searches the database based on the received features.

[0812] Input: A data packet containing the received feature data.

[0813] Operation: The server analyzes the features and uses the index to search for the corresponding item data in the database. The search result is the purchase price information.

[0814] Output: Purchase price information data for each item.

[0815] Step 7:

[0816] The server transmits the acquired price information to the terminal.

[0817] Input: Purchase price information data for each item.

[0818] How it works: The server reassembles the acquired price information into data packets and sends them to the device using the HTTP protocol, retransmitting and acknowledging them as necessary to ensure communication stability.

[0819] Output: A data packet containing the buy price information.

[0820] Step 8:

[0821] The terminal parses the price information and prepares the data for display on the user interface.

[0822] Input: A data packet containing the buy price information for each item.

[0823] What it does: Formats the retrieved pricing information based on your user interface design and displays it in a visually easy-to-understand format (e.g., graph or comparison table).

[0824] Output: Price display data on the user interface.

[0825] Step 9:

[0826] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed using an emotion engine.

[0827] Input: User's facial expression and voice data.

[0828] How it works: It acquires video data from the camera and audio data from the microphone, and uses emotion analysis algorithms (e.g., facial recognition and voice emotion identification technology) to recognize the user's emotions.

[0829] Output: User's emotional state data.

[0830] Step 10:

[0831] The device adjusts the user interface based on the recognized emotion.

[0832] Input: User emotional state data.

[0833] How it works: If the system determines that the user is dissatisfied, it automatically displays additional offers and price negotiation suggestions, and customizes interface elements and messages based on emotion recognition results.

[0834] Output: Adjusted user interface display data.

[0835] (Application example 2)

[0836] 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."

[0837] In conventional product value evaluation systems, when a user evaluates the value of an item, the system simply automatically displays the market price for the item, and does not take appropriate action that takes into account the user's feelings or satisfaction. As a result, if the user is dissatisfied with the system's display or wishes to negotiate further, the system is unable to respond, resulting in a poor user experience.

[0838] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an imaging means for allowing a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting features of each identified item; a transmission means for transmitting the extracted features to the server; a search means for the server to search for corresponding information in a database based on the features and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition means for capturing the user's facial expressions with a camera and collecting voice with a microphone to recognize emotions; and an adjustment means for adjusting the interface for the user based on the emotion recognized by the emotion recognition means. This makes it possible to automatically make additional offers or propose price negotiations even if the user is not satisfied with the offered price, thereby improving the user experience.

[0839] "Photographing means" refers to a device or function that allows a user to photograph an image of an item.

[0840] The "pre-processing means" is a function for performing pre-processing such as noise removal and light adjustment on the captured image.

[0841] "Identification means" refers to algorithms or functions that identify individual items from pre-processed images.

[0842] The "feature extraction means" is a function for extracting information such as character recognition, shape features, and color features from the identified article.

[0843] The "transmission means" is a communication means for transmitting the extracted feature amount to the server.

[0844] The "search means" is a function for searching the database based on the feature amount received by the server and acquiring corresponding information.

[0845] The "display means" is an interface or device for visually notifying the user of the acquired purchase price information.

[0846] The "emotion recognition means" is a function for recognizing emotions by analyzing the user's facial expressions and voice.

[0847] The "adjustment means" is a function for adjusting the user interface based on the emotion recognized by the emotion recognition means.

[0848] The present invention provides a system that allows users to quickly and efficiently evaluate the value of items, incorporating an emotion engine. Using photos taken by the user, AI automatically displays the market price for the item, and is also able to recognize the user's emotions and optimize the evaluation process. An embodiment of this system is shown below.

[0849] System configuration

[0850] 1. Photography: The user uses the camera on their smartphone or tablet to take a picture of the item.

[0851] 2. Pre-processing: The captured images are pre-processed using OpenCV to remove noise, adjust lighting, and optimize resolution.

[0852] 3. Identification: We use the TensorFlow Object Detection API to identify each object individually from the preprocessed image.

[0853] 4. Feature extraction: Using Tesseract OCR or custom CNN, features such as character recognition, shape features, and color features of the identified items are extracted.

[0854] 5. Transmission means: The extracted features are compressed into data packets and transmitted to the server using a communication protocol (e.g., HTTP / HTTPS).

[0855] 6. Search method: The server uses MongoDB to search for corresponding information in the database based on the received features and obtains the purchase price information.

[0856] 7. Display means: The acquired purchase price information is sent to the terminal and visually displayed on the user interface.

[0857] 8. Emotion recognition: Using Microsoft Azure Face API and Google Cloud Speech-to-Text, the user's facial expressions and voice are collected and emotions are recognized.

[0858] 9. Adjustment: Dynamically adjust the user interface based on emotions recognized by the emotion engine, such as negotiating prices or presenting additional offers.

[0859] Specific examples

[0860] Trading card valuation and emotional response

[0861] A user arranges multiple trading cards on a table at home and takes a photo with their smartphone camera. After taking the photo, the device preprocesses the image and identifies each card using an item detection algorithm. Next, Tesseract OCR extracts features such as the card name and series number, and sends the data packet to the server. The server uses MongoDB to search for the repurchase price information for the corresponding card and sends the information to the device. The device then displays the obtained price information on its user interface. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, and recognizes their emotions using the Microsoft Azure Face API or Google Cloud Speech-to-Text. If the recognized emotion is determined to be "unhappy," the system immediately presents additional offers or price negotiations.

[0862] Prompt Sentence Examples

[0863] Example prompt for Tesseract OCR:

[0864] Recognize text from image: "Figure name", "Manufacturer", "Series number"

[0865] In this way, the system of the present invention can quickly and efficiently evaluate the value of an item to the user and provide appropriate actions that take emotions into consideration.

[0866] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0867] Step 1:

[0868] The user launches the app and takes a photo of the item using the smartphone's camera. The image is then input into the device. The user follows the app's instructions to take a photo of the item under appropriate lighting.

[0869] Step 2:

[0870] The device receives the captured image and uses OpenCV to perform preprocessing such as noise removal, light adjustment, and resolution optimization. The input of the preprocessing is the captured image, and the output is the preprocessed image. Specifically, image filtering and contrast adjustment are performed.

[0871] Step 3:

[0872] The device analyzes the preprocessed image using the TensorFlow Object Detection API and uses a discriminator to identify individual objects. The input is the preprocessed image, and the output is a bounding box indicating the location of the object. The algorithm detects features in the image and identifies the area of ​​each object.

[0873] Step 4:

[0874] For each identified item, the device uses Tesseract OCR and a custom CNN to extract features such as character recognition, shape features, and color features. The input is the image region for each item, and the output is the extracted features. OCR reads the character information, and CNN analyzes the shape and color patterns.

[0875] Step 5:

[0876] The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP / HTTPS). The input is the features, and the output is the data packets sent to the server. This includes data compression and encryption.

[0877] Step 6:

[0878] The server uses MongoDB to search the database based on the received features and obtain the corresponding purchase price information. The input is the received features and the output is the obtained purchase price information. A database search algorithm is executed to extract the optimal price information.

[0879] Step 7:

[0880] The server sends the acquired purchase price information to the terminal. The input is the price information, and the output is the data sent to the terminal. Again, a communication protocol (e.g., HTTP / HTTPS) is used.

[0881] Step 8:

[0882] The terminal parses the received price information and prepares the data for visual display in the user interface. The input is the price information and the output is the information displayed to the user. It displays the information in a visually easy-to-understand graphical format.

[0883] Step 9:

[0884] The device uses a camera and microphone to collect the user's facial expressions and voice, and recognizes emotions using the Microsoft Azure Face API and Google Cloud Speech-to-Text. The input is a photo of the user's facial expression and voice, and the output is analyzed emotional information. The emotion recognition algorithm analyzes the data and identifies the user's emotion.

[0885] Step 10:

[0886] The device executes an adjustment method to adjust the user interface based on the recognized emotion. The input is emotion information, and the output is an adjusted user interface. Specifically, if satisfaction is low, price negotiation or additional offers are presented.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] [Third embodiment]

[0891] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0892] 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.

[0893] 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).

[0894] 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.

[0895] 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.

[0896] 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).

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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."

[0903] This invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. Specifically, this describes a system in which a user takes a photo and an AI automatically displays the market price for the item using the photo.

[0904] System Overview

[0905] The system consists of the following main components:

[0906] 1. A means for users to take pictures

[0907] 2. Preprocessing means for preprocessing the captured images

[0908] 3. A means of identifying each item individually from the preprocessed image.

[0909] 4. Feature extraction method to extract the features of each item

[0910] 5. A method for transmitting extracted features to the server

[0911] 6. A search method to search the database on the server and obtain purchase price information

[0912] 7. Display means for receiving the acquired price information and displaying it on the user interface

[0913] Program processing

[0914] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[0915] 2. The device receives the captured image and performs preprocessing, which includes noise reduction, image normalization, and light adjustment.

[0916] 3. The device analyzes the preprocessed image and uses a classification method to identify individual objects. This process uses an object detection algorithm (such as YOLO or Faster R-CNN), identifies the object's bounding box, and separates each object.

[0917] 4. The terminal extracts features from the identified items. Feature extraction methods include character recognition (OCR), shape features, and color features of the items.

[0918] 5. The device sends the extracted features to the server using a communication protocol over the Internet (e.g., HTTP or WebSocket).

[0919] 6. The server searches the database based on the received feature values. The search means uses the feature values ​​as a key to match the product information in the database and obtains the corresponding purchase price information.

[0920] 7. The server sends the acquired price information to the device, allowing the user to check the value of each item through the app.

[0921] 8. The terminal analyzes the received price information and prepares it for display on a user interface, including an interface that visually presents the price information to the user in a grid or list format.

[0922] Specific examples

[0923] Below are some specific usage examples.

[0924] Example: Determining the buyback price of a trading card

[0925] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[0926] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[0927] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[0928] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[0929] 5. The device aggregates the extracted features and sends them to the server.

[0930] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[0931] 7. The server sends the acquired market price information to the terminal.

[0932] 8. The terminal displays the price information on the user interface and provides it to the user.

[0933] This flow allows users to quickly and easily check the buying price of trading cards and decide whether to sell them.

[0934] This system allows users to efficiently evaluate the value of multiple items at once, significantly reducing time and effort.

[0935] The processing flow will be explained below.

[0936] Step 1:

[0937] Users launch the app and use the camera to take a photo of the item they want to sell. They can also take a photo of multiple items at once.

[0938] Step 2:

[0939] The device receives the captured image and performs pre-processing such as noise removal, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[0940] Step 3:

[0941] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, which generates a bounding box to uniquely identify each object.

[0942] Step 4:

[0943] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[0944] Step 5:

[0945] The device compresses the extracted features into data packets and transmits them to a server over the Internet, using common communication protocols (e.g., HTTP or WebSocket) to ensure reliable and fast transmission.

[0946] Step 6:

[0947] The server analyzes the received feature data and searches the database. This search is performed by using the feature as a key to match the corresponding product information in the database.

[0948] Step 7:

[0949] The server retrieves market price information for the relevant items from its database and compiles it into a data packet, which identifies how much each item is worth.

[0950] Step 8:

[0951] The server sends the acquired market price information to the terminal, using a protocol (e.g., HTTPS) to ensure data integrity and communication stability.

[0952] Step 9:

[0953] The terminal analyzes the received market price information and prepares the data for display on the user interface, where the data is arranged in a format that is intuitively understandable to the user (for example, a grid or list format).

[0954] Step 10:

[0955] The terminal displays market price information on the user interface, allowing the user to visually confirm the value of each item.

[0956] Step 11:

[0957] The user can decide whether to sell or dispose of the item based on the displayed market price information, and can take further action (for example, proceed with the sale procedure) if necessary.

[0958] This detailed flow allows users to efficiently assess the value of items with minimal work and take appropriate action immediately.

[0959] Example 1

[0960] 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."

[0961] In the past, it was difficult to quickly and accurately assess the value of items, and users had to manually assess the value of each item, which required a significant amount of time and effort. Furthermore, manual assessments were inaccurate, making it difficult to obtain an accurate purchase price. To solve these problems, a system was needed that could take photos of items, automatically analyze the images, and display the purchase price.

[0962] 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.

[0963] In this invention, the server includes an imaging means for a user to capture images of multiple items, a preprocessing means for performing noise removal, image normalization, and light adjustment on the captured images, an identification means for individually identifying each item from the preprocessed images using an item detection algorithm and specifying the area, a feature extraction means for extracting character recognition, shape features, and color features as feature quantities of each identified item, a transmission means for transmitting the extracted feature quantities via the Internet, a search means for the server to search information in a database based on the feature quantities and obtain purchase price information, and a display means for receiving the obtained purchase price information and visually presenting it to the user, thereby enabling the user to quickly and accurately evaluate the value of the items.

[0964] "User" refers to the person who takes an image of an item of interest and inputs it into the system.

[0965] An "item" is an object whose value a user wishes to assess through the system.

[0966] "Photographing means" refers to a device such as a camera that a user uses to take an image of an item.

[0967] The "pre-processing means" refers to a means for performing noise removal, image normalization, and light adjustment on the captured image.

[0968] "Identification means" refers to a means for individually identifying each item and specifying its region from the preprocessed image using an item detection algorithm.

[0969] "Features" refers to information such as character recognition, shape features, and color features extracted from each identified item.

[0970] The "feature extraction means" refers to a means for extracting the feature amount of each identified item.

[0971] The "transmission means" refers to a means for transmitting the extracted feature amount to a server via the Internet.

[0972] The "search means" refers to a means for searching the database based on the feature amount received by the server and obtaining purchase price information.

[0973] "Database" refers to an information aggregation system that stores information on various items and purchase prices.

[0974] The "display means" refers to a means for visually presenting the acquired purchase price information to the user.

[0975] "Noise reduction" refers to a process for removing unnecessary noise from a captured image.

[0976] "Image normalization" refers to the process of adjusting the size and color tone of a captured image to a certain standard.

[0977] "Light adjustment" refers to the process of appropriately adjusting the brightness and contrast of a captured image.

[0978] "Item detection algorithm" refers to a computational method for detecting item regions in an image.

[0979] "Optical character recognition technology" refers to technology for extracting text information from images.

[0980] A "convolutional neural network" is a type of deep learning technology used in image analysis.

[0981] MODE FOR CARRYING OUT THE INVENTION

[0982] The present invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. This system allows users to take a photo of the item, and AI automatically displays the market price for the item using the photo. The following describes in detail the embodiments of the present invention.

[0983] System Overview

[0984] The system consists of the following main components:

[0985] 1. A means for users to take pictures

[0986] 2. Pre-processing means for noise reduction, image normalization, and light adjustment of the captured images.

[0987] 3. Identification means for individually identifying each item and identifying its area using an item detection algorithm from the preprocessed image.

[0988] 4. Feature extraction means for extracting character recognition (OCR), shape features, and color features as features of each item

[0989] 5. A means for transmitting the extracted features to a server via the Internet

[0990] 6. A search method for searching the database based on the feature values ​​received by the server and obtaining purchase price information

[0991] 7. Display means for visually presenting the acquired price information to the user

[0992] Hardware and software used

[0993] This system uses the following hardware and software:

[0994] User device: smartphone or tablet

[0995] Camera module: A camera built into a smartphone or tablet

[0996] Pre-processing software: Image processing library (e.g., OpenCV)

[0997] Object detection algorithms: YOLO (You Only Look Once) and Faster R-CNN

[0998] Character Recognition Software (OCR): Tesseract OCR

[0999] Communication protocol: HTTP or WebSocket

[1000] Server: Cloud server, database management system (e.g. MySQL)

[1001] Display software: User interface library (e.g., React, Vue.js)

[1002] Specific examples of explanation

[1003] Next, a specific example of use is shown.

[1004] Example: Determining the buyback price of a trading card

[1005] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[1006] 2. The device receives the photo and performs preprocessing such as noise reduction and light adjustment using the OpenCV image processing library.

[1007] 3. The device uses an object detection algorithm (such as YOLO or Faster R-CNN) to identify the location of each card and extract each card individually.

[1008] 4. The terminal extracts features such as the card name and series number from each card using Tesseract OCR or similar.

[1009] 5. The device aggregates the extracted features and sends them to the server using HTTP or WebSocket.

[1010] 6. The server searches the internal database (MySQL) based on the features and obtains the market price for buying each card.

[1011] 7. The server sends the acquired market price information in JSON format to the terminal.

[1012] 8. The device displays the price information in a user interface (e.g., React or Vue.js) and provides it to the user.

[1013] Prompt Sentence Examples

[1014] "Users take photos of multiple trading cards with their smartphone camera."

[1015] "The device will remove noise from the captured image and adjust the lighting."

[1016] "The terminal uses an object detection algorithm to determine the location of each card."

[1017] The device uses Tesseract OCR to extract features and sends them to the server.

[1018] The server searches its internal database based on the features and obtains the purchase price information.

[1019] "The terminal displays information on a user interface and provides it to the user."

[1020] In this way, by using the system of the present invention, users can quickly and accurately evaluate the market price for buying an item.

[1021] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1022] Step 1:

[1023] The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they wish to sell. The input is image data acquired through the camera, and the output is the captured image file. Specifically, the user operates the camera app to take a photo of an item (e.g., a trading card).

[1024] Step 2:

[1025] The device receives images taken by the user and performs preprocessing. The input is the captured image file, and the output is preprocessed image data. Preprocessing involves applying a noise reduction filter, normalizing the image, and adjusting the brightness. Specifically, noise reduction and brightness adjustment are performed automatically using an image processing library (e.g., OpenCV).

[1026] Step 3:

[1027] The device analyzes the preprocessed image and identifies the object using an object detection algorithm (e.g., YOLO or Faster R-CNN). The input is the preprocessed image data, and the output is the bounding box (area) of the object. Specifically, the device runs the algorithm to detect the object in the image and obtain its coordinate information.

[1028] Step 4:

[1029] The device extracts features from each identified object. The input is image data including the object's bounding box, and the output is the extracted features (e.g., character, shape, color). Feature extraction involves character recognition (OCR), shape analysis, and color feature extraction. Specifically, it uses Tesseract OCR to extract character information and executes a shape analysis algorithm.

[1030] Step 5:

[1031] The device collects the extracted features and sends them to a server via the Internet. The input is the feature data (e.g., in JSON format), and the output is the results sent to the server. Specifically, the data is sent to the server using HTTP or WebSocket protocol.

[1032] Step 6:

[1033] The server searches the database based on the received features. The input is the feature data sent from the device, and the output is the purchase price information as a search result. Specifically, it generates a database query and compares it with the internal item information.

[1034] Step 7:

[1035] The server obtains purchase price information and sends it to the terminal. The input is the purchase price information from the database, and the output is the price information (e.g., JSON format) sent to the terminal. Specifically, it formats the obtained price information and sends it to the terminal via the Internet.

[1036] Step 8:

[1037] The terminal analyzes the received price information and prepares it for display on the user interface. The input is the price information received from the server (e.g., JSON format), and the output is the visual information displayed on the user interface. Specifically, the price information is displayed in list format on the screen using React and Vue.js.

[1038] (Application example 1)

[1039] 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."

[1040] Conventional product purchase price appraisal systems have the drawback of requiring users to input product information individually, which is time-consuming and laborious. Furthermore, the lack of highly accurate product identification and feature extraction results inconsistent appraisal results. Furthermore, the user interface is not intuitive, making it difficult for users to use.

[1041] 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.

[1042] In this invention, the server includes a means for preprocessing images taken by a user and identifying the items, a means for extracting feature quantities of the items, and a means for searching a database to obtain buy price information. This allows for efficient preprocessing and identification even when a user takes photos of multiple items at once, making it possible to quickly provide accurate buy price information.

[1043] The "photography means" is a device or function for taking an image of an item that a user wishes to sell, and typically uses a camera built into a smart device.

[1044] The "pre-processing means" is a device or function that performs processes such as noise removal, image normalization, and lighting adjustment on the captured image.

[1045] "Identification means" is a device or function that uniquely identifies each item from the pre-processed image, typically using an object detection algorithm.

[1046] The "feature extraction means" is a device or function that extracts feature quantities from each identified item, and includes character recognition, shape features, color features, and the like.

[1047] The "transmission means" is a device or function for transmitting the extracted features to a server, and uses HTTP or WebSocket as a communication protocol.

[1048] The "search means" is a device or function that searches the database in the server and obtains the purchase price information of the corresponding item.

[1049] The "display means" is a device or function that displays the acquired purchase price information to the user, and provides the information visually via a user interface.

[1050] An "artificial intelligence model" is a trained algorithm or technology used for item identification or feature extraction, including YOLO and OCR.

[1051] "Smart device" is a general term for electronic devices such as mobile phones and tablet devices equipped with a camera function.

[1052] The system embodying the present invention allows a user to take an image of an item they wish to sell, and then quickly and efficiently evaluates the purchase price of the item based on the image. A specific embodiment of the system will be described below.

[1053] System Configuration

[1054] The system mainly consists of the following components:

[1055] 1. Photography Method:

[1056] It is a means for a user to take an image of an item, typically using a camera built into a smart device.

[1057] 2. Pretreatment methods:

[1058] It is a means of preprocessing the captured images such as noise removal, image normalization, lighting adjustment, etc. This is performed using the OpenCV library.

[1059] 3. Means of identification:

[1060] It is a means of identifying objects from preprocessed images and uses the YOLO (You Only Look Once) algorithm to detect objects.

[1061] 4. Feature extraction method:

[1062] This is a means of extracting features from identified items, including character recognition (OCR), shape features, color features, etc. Specifically, the Tesseract OCR engine is used.

[1063] 5. Means of transmission:

[1064] This is a means for sending extracted features to a server, and uses HTTP or WebSocket as the communication protocol.

[1065] 6. Search Methods:

[1066] This is a method of searching a database based on the feature values ​​received on the server side and obtaining the purchase price information for the corresponding item. The database can be, for example, PostgreSQL or MySQL.

[1067] 7. Display means:

[1068] This is a means of displaying the acquired purchase price information to the user, and provides the information visually through a user interface, using UI libraries such as React Native and Flutter.

[1069] Hardware and Software Configuration

[1070] Hardware:

[1071] Smart devices (mobile devices with camera functions and tablet devices)

[1072] Server (Cloud server or on-premise server)

[1073] software:

[1074] OpenCV (image preprocessing)

[1075] YOLO (article identification)

[1076] Tesseract OCR engine (feature extraction)

[1077] HTTP / WebSocket (communication protocol)

[1078] Database (PostgreSQL, MySQL, etc.)

[1079] React Native or Flutter (user interface)

[1080] Specific examples

[1081] Take the example of a user selling a smartphone that they no longer need.

[1082] 1. The user launches the app and takes a picture of the smartphone they want to sell.

[1083] 2. The device receives the image and uses OpenCV to remove noise and adjust the lighting.

[1084] 3. The device uses YOLO to identify the smartphone and uses the Tesseract OCR engine to extract features such as the model name and serial number.

[1085] 4. The device sends the features to the server via the HTTP protocol.

[1086] 5. The server searches the database based on the received features and obtains the purchase price information.

[1087] 6. The server sends the acquired data to the terminal.

[1088] 7. The terminal uses React Native or Flutter to display pricing information in the user interface.

[1089] Prompt Sentence Examples

[1090] As an example of how a user might give instructions to the system, the following prompt sentences might be used:

[1091] "Please take a photo on your smartphone and let us know the purchase price."

[1092] This system allows users to quickly and accurately find out the purchase price of their items, significantly reducing the amount of time and effort required.

[1093] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1094] Step 1:

[1095] A user uses the camera on their smart device to take a picture of the item they want to sell (e.g., a smartphone, accessory, etc.). The input is the image of the item taken by the user, and the output is the captured image file.

[1096] Step 2:

[1097] The device receives the captured image and performs preprocessing. The input is the captured image file, and the output is the preprocessed image. This preprocessing includes specific operations such as noise removal, image normalization, and lighting adjustment using the OpenCV library.

[1098] Step 3:

[1099] The device analyzes the preprocessed image and identifies the object using the YOLO algorithm. The input is the preprocessed image, and the output is the object's location (bounding box). Specifically, the YOLO model identifies the object's area.

[1100] Step 4:

[1101] The device extracts features from the identified items. The input is an image with the item's location information, and the output is the extracted features (e.g., text information, color features, shape features). Specifically, the Tesseract OCR engine is used to extract the item's model name and serial number as text data.

[1102] Step 5:

[1103] The device sends the extracted features to the server using HTTP or WebSocket protocol. The input is the extracted feature data, and the output is a message that the transmission was successful. The specific operation includes encoding the features as parameters of an HTTP POST request and sending them over the network.

[1104] Step 6:

[1105] The server searches the database based on the received features and obtains the corresponding purchase price information. The input is feature data, and the output is purchase price information. Specific data processing involves using SQL queries to search for related information in the database and obtain the corresponding purchase price information.

[1106] Step 7:

[1107] The server sends the acquired purchase price information to the terminal. The input is the purchase price information, and the output is a message that transmission to the terminal was successful. Specific operations include formatting the price information as an HTTP response and sending it back over the network.

[1108] Step 8:

[1109] The terminal displays the received price information on a user interface. The input is the purchase price information received from the server, and the output is the price information visually displayed to the user. Specific operations include updating UI components using React Native or Flutter.

[1110] This series of processes allows the user to quickly find out the selling price of the item.

[1111] 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.

[1112] This invention combines an emotion engine with a system that allows users to quickly and efficiently evaluate the value of an item. Users take a photo, and the AI ​​not only automatically displays the market price for the item using that photo, but also recognizes the user's emotions and optimizes the evaluation process.

[1113] System Overview

[1114] The system consists of the following main components:

[1115] 1. A means for users to take pictures

[1116] 2. Preprocessing means for preprocessing the captured images

[1117] 3. A means of identifying each item individually from the preprocessed image.

[1118] 4. Feature extraction method to extract the features of each item

[1119] 5. A method for transmitting extracted features to the server

[1120] 6. A search method to search the database on the server and obtain purchase price information

[1121] 7. Display means for receiving the acquired price information and displaying it on the user interface

[1122] 8. Emotion engine that recognizes emotions by analyzing the user's facial expressions and voice

[1123] Program processing

[1124] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[1125] 2. The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[1126] 3. The device analyzes the preprocessed image and uses the discrimination means to identify individual items. It uses an item detection algorithm to locate each item and generate a bounding box.

[1127] 4. For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[1128] 5. The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP or WebSocket).

[1129] 6. The server searches the database based on the received feature values, matches the corresponding item information in the database using the feature values ​​as a key, and obtains the purchase price information.

[1130] 7. The server sends the acquired price information to the terminal, using an appropriate protocol to ensure data reliability and communication stability.

[1131] 8. The terminal parses the price information and prepares the data for display on the user interface in a visually friendly format.

[1132] 9. The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[1133] 10. The device will adapt the user interface based on the perceived emotion. For example, it could be designed to negotiate a price or offer additional services if it determines the user is dissatisfied.

[1134] Specific examples

[1135] Below are some specific usage examples.

[1136] Example: Determining the buyback price of trading cards and taking user sentiment into account

[1137] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[1138] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[1139] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[1140] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[1141] 5. The device sends the extracted features to the server.

[1142] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[1143] 7. The server sends the acquired market price information to the terminal.

[1144] 8. The terminal displays the price information on the user interface.

[1145] 9. The device captures the user's facial expressions with a camera and collects audio with a microphone, which is then analyzed by an emotion engine.

[1146] 10. If the device determines using its emotion engine that the user is not satisfied, it will make additional offers or negotiate.

[1147] This flow allows users to quickly and easily check the buying price of trading cards and take appropriate action based on their feelings. This system allows users to efficiently evaluate the value of items with minimal work and take appropriate action immediately.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] Users launch the app and use the camera function to take a photo of the item they want to sell, using the camera on their smartphone or tablet.

[1151] Step 2:

[1152] The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization, thereby improving the accuracy of image recognition.

[1153] Step 3:

[1154] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, generating a bounding box that identifies each object individually.

[1155] Step 4:

[1156] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[1157] Step 5:

[1158] The device compresses the extracted features into data packets and sends them to a server over the Internet using a communication protocol (e.g., HTTP or WebSocket).

[1159] Step 6:

[1160] The server analyzes the received feature data and searches the database. Using the feature data as a key, it matches the corresponding item information in the database and obtains the purchase price information.

[1161] Step 7:

[1162] The server sends the acquired purchase price information to the terminal, using an appropriate protocol such as HTTPS to ensure data reliability and communication stability.

[1163] Step 8:

[1164] The terminal parses the received price information and prepares the data for display on the user interface in a visually friendly format.

[1165] Step 9:

[1166] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[1167] Step 10:

[1168] The device adjusts the user interface and display content based on the user's emotions recognized by the emotion engine. For example, if the device determines that the user is dissatisfied, it is designed to offer price negotiations or additional offers.

[1169] Step 11:

[1170] Users can then review the final displayed price information and any additional offers and decide whether to sell or dispose of the item. If necessary, they can proceed with the sale process within the app.

[1171] This system not only allows users to efficiently evaluate the value of an item by taking a photo and analyzing emotions once, but also allows them to receive the most appropriate service based on their emotions.

[1172] Example 2

[1173] 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."

[1174] In conventional purchase price evaluation systems, even if a user takes a picture of an item, the system does not accurately identify the item or extract its features, often resulting in an incorrect price evaluation. Furthermore, because the system does not take into account the user's feelings, it does not take appropriate action even if the user is dissatisfied. This leads to the problem of reduced user convenience and satisfaction.

[1175] 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: an imaging means for a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting feature values ​​for each identified item; a transmission means for transmitting the extracted feature values ​​to the server; a search means for the server to search for corresponding information in a database based on the feature values ​​and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition acquisition means for acquiring the user's facial expressions and voice to recognize emotions; and an interface adjustment means for adjusting the user interface based on the results of the emotion recognition. This makes it possible to accurately extract feature values ​​from images of items captured by the user, present an appropriate buy price, and adjust the interface according to the user's satisfaction.

[1176] "Photographing means" refers to a device or function that allows a user to take images of multiple items.

[1177] "Pre-processing means" refers to processing of captured images, such as noise removal, light adjustment, and resolution optimization.

[1178] "Identification means" refers to algorithms or functions that uniquely identify and locate each item in the pre-processed image.

[1179] "Feature extraction means" refers to technology or algorithms for extracting features such as character recognition, shape features, and color features of each identified item.

[1180] The "transmission means" refers to a device or communication protocol for transmitting the extracted feature quantity to a server.

[1181] "Search means" refers to a technology or system for searching for corresponding information in a database based on the features received by the server and obtaining purchase price information.

[1182] The "display means" refers to a device or interface for displaying the acquired purchase price information to the user.

[1183] "Acquisition means for emotion recognition" refers to devices or technologies for acquiring a user's facial expressions and voice and recognizing emotions.

[1184] "Interface adjustment means" refers to functions and technologies for adjusting the user interface based on the results of emotion recognition.

[1185] The present invention combines an emotion recognition engine with a system that allows users to quickly and efficiently evaluate the value of an item. Specific embodiments of this system will be described below.

[1186] System Overview

[1187] The present invention is a system that allows users to take images of multiple items using the camera on their smartphone or tablet, extracts features of the items based on the images, and displays the purchase price via a server.Furthermore, it has the feature of recognizing the user's emotions and adjusting the interface appropriately.

[1188] Hardware and software used

[1189] Image capture method: The built-in camera of a smartphone or tablet. This hardware is used to capture images of the item.

[1190] Pre-processing means: Image processing library (e.g. OpenCV). This software denoises the images, adjusts lighting, and optimizes resolution.

[1191] Identification method: Item detection algorithms (e.g., YOLO, SSD). These algorithms identify individual items from pre-processed images.

[1192] Feature extraction methods: OCR software and algorithms for extracting shape and color features. Used to extract the features of an object.

[1193] Transmission method: A communication protocol such as HTTP or WebSocket. The extracted features are sent to the server using this protocol.

[1194] Search method: Database search algorithm. The server searches for corresponding information in the database based on the features received and obtains the purchase price.

[1195] Display: The display device of a smartphone or tablet. This device is used to display the buyback price to the user.

[1196] Acquisition means for emotion recognition: Camera and microphone. These hardware capture the user's facial expressions and voice.

[1197] Emotion Recognition Engine: Machine learning algorithms (e.g., facial recognition, voice emotion identification technology) used to analyze user emotions.

[1198] Interface adjustment means: A software library for dynamically changing the user interface. The interface is adjusted based on the results of emotion recognition.

[1199] Specific examples

[1200] Below are some specific usage examples.

[1201] Example: Determining the buyback price of trading cards and taking user sentiment into account

[1202] 1. User: Arranges several trading cards from home on a table and takes a photo using the camera on their smartphone.

[1203] 2. Device: Receives the photo and uses an image processing library to perform preprocessing such as noise reduction and light adjustment.

[1204] 3. Terminal: Using an object detection algorithm, each card is located and individually cut out from the preprocessed image.

[1205] 4. Terminal: Using OCR software, extract features such as the card name and serial number from each card.

[1206] 5. Terminal: The extracted features are sent to the server using the HTTP protocol.

[1207] 6. Server: Search the database based on the features and obtain the market price for buying each card.

[1208] 7. Server: Sends the acquired market price information to the terminal using the HTTP protocol.

[1209] 8. Terminal: Display price information in a user interface.

[1210] 9. Device: Using a camera and microphone, the user's facial expressions and voice are captured and analyzed using an emotion recognition engine.

[1211] 10. Terminal: Based on the results of emotion recognition, if the user is not satisfied, the terminal will suggest additional offers or price negotiations.

[1212] Examples of prompts for generative AI models

[1213] Here are some example prompts to input to the generative AI model:

[1214] "Please describe in detail a system that takes a user's photo of a trading card, displays the market value of each card, and suggests additional offers based on the user's sentiment. Please also provide a detailed description of each processing step and its specific behavior."

[1215] This prompt allows the generative AI model to provide a detailed explanation of each processing step of the system and the specific actions that accompany it.

[1216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] The user launches the app and uses the camera to take a photo of the item they want to sell.

[1219] Input: An image taken by a user using the camera on their smartphone or tablet.

[1220] How it works: The user launches the app, selects the camera function, points the camera at an object, and presses the shutter button.

[1221] Output: Image data of the item.

[1222] Step 2:

[1223] The device receives the captured images and performs pre-processing such as noise removal, light adjustment, and resolution optimization.

[1224] Input: Image data of photographed item.

[1225] What it does: It uses an image processing library (e.g. OpenCV) to remove noise from the image, adjust color and brightness, and optionally resize and optimize the resolution of the image.

[1226] Output: Preprocessed image data.

[1227] Step 3:

[1228] The terminal analyzes the pre-processed images and identifies individual items using the identification means.

[1229] Input: Preprocessed image data.

[1230] How it works: Uses an object detection algorithm (e.g., YOLO or SSD) to locate the object and generate a bounding box.

[1231] Output: Bounding box coordinate data for each object.

[1232] Step 4:

[1233] The terminal extracts features for each identified item using a feature extraction algorithm.

[1234] Input: Bounding box coordinate data and preprocessed image data for each object.

[1235] How it works: It uses OCR (Optical Character Recognition) software to extract text information from an object, as well as analyze features such as color and shape, and saves them as a digital signature.

[1236] Output: Feature data for each item.

[1237] Step 5:

[1238] The terminal compresses the extracted features into a data packet and transmits it to the server.

[1239] Input: Feature data for each item.

[1240] How it works: The feature data is efficiently compressed using a compression algorithm and sent to a server using the HTTP protocol.

[1241] Output: A data packet containing feature data.

[1242] Step 6:

[1243] The server searches the database based on the received features.

[1244] Input: A data packet containing the received feature data.

[1245] Operation: The server analyzes the features and uses the index to search for the corresponding item data in the database. The search result is the purchase price information.

[1246] Output: Purchase price information data for each item.

[1247] Step 7:

[1248] The server transmits the acquired price information to the terminal.

[1249] Input: Purchase price information data for each item.

[1250] How it works: The server reassembles the acquired price information into data packets and sends them to the device using the HTTP protocol, retransmitting and acknowledging them as necessary to ensure communication stability.

[1251] Output: A data packet containing the buy price information.

[1252] Step 8:

[1253] The terminal parses the price information and prepares the data for display on the user interface.

[1254] Input: A data packet containing the buy price information for each item.

[1255] What it does: Formats the retrieved pricing information based on your user interface design and displays it in a visually easy-to-understand format (e.g., graph or comparison table).

[1256] Output: Price display data on the user interface.

[1257] Step 9:

[1258] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed using an emotion engine.

[1259] Input: User's facial expression and voice data.

[1260] How it works: It acquires video data from the camera and audio data from the microphone, and uses emotion analysis algorithms (e.g., facial recognition and voice emotion identification technology) to recognize the user's emotions.

[1261] Output: User's emotional state data.

[1262] Step 10:

[1263] The device adjusts the user interface based on the recognized emotion.

[1264] Input: User emotional state data.

[1265] How it works: If the system determines that the user is dissatisfied, it automatically displays additional offers and price negotiation suggestions, and customizes interface elements and messages based on emotion recognition results.

[1266] Output: Adjusted user interface display data.

[1267] (Application example 2)

[1268] 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."

[1269] In conventional product value evaluation systems, when a user evaluates the value of an item, the system simply automatically displays the market price for the item, and does not take appropriate action that takes into account the user's feelings or satisfaction. As a result, if the user is dissatisfied with the system's display or wishes to negotiate further, the system is unable to respond, resulting in a poor user experience.

[1270] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an imaging means for allowing a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting features of each identified item; a transmission means for transmitting the extracted features to the server; a search means for the server to search for corresponding information in a database based on the features and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition means for capturing the user's facial expressions with a camera and collecting voice with a microphone to recognize emotions; and an adjustment means for adjusting the interface for the user based on the emotion recognized by the emotion recognition means. This makes it possible to automatically make additional offers or propose price negotiations even if the user is not satisfied with the offered price, thereby improving the user experience.

[1271] "Photographing means" refers to a device or function that allows a user to photograph an image of an item.

[1272] The "pre-processing means" is a function for performing pre-processing such as noise removal and light adjustment on the captured image.

[1273] "Identification means" refers to algorithms or functions that identify individual items from pre-processed images.

[1274] The "feature extraction means" is a function for extracting information such as character recognition, shape features, and color features from the identified article.

[1275] The "transmission means" is a communication means for transmitting the extracted feature amount to the server.

[1276] The "search means" is a function for searching the database based on the feature amount received by the server and acquiring corresponding information.

[1277] The "display means" is an interface or device for visually notifying the user of the acquired purchase price information.

[1278] The "emotion recognition means" is a function for recognizing emotions by analyzing the user's facial expressions and voice.

[1279] The "adjustment means" is a function for adjusting the user interface based on the emotion recognized by the emotion recognition means.

[1280] The present invention provides a system that allows users to quickly and efficiently evaluate the value of items, incorporating an emotion engine. Using photos taken by the user, AI automatically displays the market price for the item, and is also able to recognize the user's emotions and optimize the evaluation process. An embodiment of this system is shown below.

[1281] System configuration

[1282] 1. Photography: The user uses the camera on their smartphone or tablet to take a picture of the item.

[1283] 2. Pre-processing: The captured images are pre-processed using OpenCV to remove noise, adjust lighting, and optimize resolution.

[1284] 3. Identification: We use the TensorFlow Object Detection API to identify each object individually from the preprocessed image.

[1285] 4. Feature extraction: Using Tesseract OCR or custom CNN, features such as character recognition, shape features, and color features of the identified items are extracted.

[1286] 5. Transmission means: The extracted features are compressed into data packets and transmitted to the server using a communication protocol (e.g., HTTP / HTTPS).

[1287] 6. Search method: The server uses MongoDB to search for corresponding information in the database based on the received features and obtains the purchase price information.

[1288] 7. Display means: The acquired purchase price information is sent to the terminal and visually displayed on the user interface.

[1289] 8. Emotion recognition: Using Microsoft Azure Face API and Google Cloud Speech-to-Text, the user's facial expressions and voice are collected and emotions are recognized.

[1290] 9. Adjustment: Dynamically adjust the user interface based on emotions recognized by the emotion engine, such as negotiating prices or presenting additional offers.

[1291] Specific examples

[1292] Trading card valuation and emotional response

[1293] A user arranges multiple trading cards on a table at home and takes a photo with their smartphone camera. After taking the photo, the device preprocesses the image and identifies each card using an item detection algorithm. Next, Tesseract OCR extracts features such as the card name and series number, and sends the data packet to the server. The server uses MongoDB to search for the repurchase price information for the corresponding card and sends the information to the device. The device then displays the obtained price information on its user interface. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, and recognizes their emotions using the Microsoft Azure Face API or Google Cloud Speech-to-Text. If the recognized emotion is determined to be "unhappy," the system immediately presents additional offers or price negotiations.

[1294] Prompt Sentence Examples

[1295] Example prompt for Tesseract OCR:

[1296] Recognize text from image: "Figure name", "Manufacturer", "Series number"

[1297] In this way, the system of the present invention can quickly and efficiently evaluate the value of an item to the user and provide appropriate actions that take emotions into consideration.

[1298] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1299] Step 1:

[1300] The user launches the app and takes a photo of the item using the smartphone's camera. The image is then input into the device. The user follows the app's instructions to take a photo of the item under appropriate lighting.

[1301] Step 2:

[1302] The device receives the captured image and uses OpenCV to perform preprocessing such as noise removal, light adjustment, and resolution optimization. The input of the preprocessing is the captured image, and the output is the preprocessed image. Specifically, image filtering and contrast adjustment are performed.

[1303] Step 3:

[1304] The device analyzes the preprocessed image using the TensorFlow Object Detection API and uses a discriminator to identify individual objects. The input is the preprocessed image, and the output is a bounding box indicating the location of the object. The algorithm detects features in the image and identifies the area of ​​each object.

[1305] Step 4:

[1306] For each identified item, the device uses Tesseract OCR and a custom CNN to extract features such as character recognition, shape features, and color features. The input is the image region for each item, and the output is the extracted features. OCR reads the character information, and CNN analyzes the shape and color patterns.

[1307] Step 5:

[1308] The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP / HTTPS). The input is the features, and the output is the data packets sent to the server. This includes data compression and encryption.

[1309] Step 6:

[1310] The server uses MongoDB to search the database based on the received features and obtain the corresponding purchase price information. The input is the received features and the output is the obtained purchase price information. A database search algorithm is executed to extract the optimal price information.

[1311] Step 7:

[1312] The server sends the acquired purchase price information to the terminal. The input is the price information, and the output is the data sent to the terminal. Again, a communication protocol (e.g., HTTP / HTTPS) is used.

[1313] Step 8:

[1314] The terminal parses the received price information and prepares the data for visual display in the user interface. The input is the price information and the output is the information displayed to the user. It displays the information in a visually easy-to-understand graphical format.

[1315] Step 9:

[1316] The device uses a camera and microphone to collect the user's facial expressions and voice, and recognizes emotions using the Microsoft Azure Face API and Google Cloud Speech-to-Text. The input is a photo of the user's facial expression and voice, and the output is analyzed emotional information. The emotion recognition algorithm analyzes the data and identifies the user's emotion.

[1317] Step 10:

[1318] The device executes an adjustment method to adjust the user interface based on the recognized emotion. The input is emotion information, and the output is an adjusted user interface. Specifically, if satisfaction is low, price negotiation or additional offers are presented.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] [Fourth embodiment]

[1323] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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).

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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."

[1336] This invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. Specifically, this describes a system in which a user takes a photo and an AI automatically displays the market price for the item using the photo.

[1337] System Overview

[1338] The system consists of the following main components:

[1339] 1. A means for users to take pictures

[1340] 2. Preprocessing means for preprocessing the captured images

[1341] 3. A means of identifying each item individually from the preprocessed image.

[1342] 4. Feature extraction method to extract the features of each item

[1343] 5. A method for transmitting extracted features to the server

[1344] 6. A search method to search the database on the server and obtain purchase price information

[1345] 7. Display means for receiving the acquired price information and displaying it on the user interface

[1346] Program processing

[1347] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[1348] 2. The device receives the captured image and performs preprocessing, which includes noise reduction, image normalization, and light adjustment.

[1349] 3. The device analyzes the preprocessed image and uses a classification method to identify individual objects. This process uses an object detection algorithm (such as YOLO or Faster R-CNN), identifies the object's bounding box, and separates each object.

[1350] 4. The terminal extracts features from the identified items. Feature extraction methods include character recognition (OCR), shape features, and color features of the items.

[1351] 5. The device sends the extracted features to the server using a communication protocol over the Internet (e.g., HTTP or WebSocket).

[1352] 6. The server searches the database based on the received feature values. The search means uses the feature values ​​as a key to match the product information in the database and obtains the corresponding purchase price information.

[1353] 7. The server sends the acquired price information to the device, allowing the user to check the value of each item through the app.

[1354] 8. The terminal analyzes the received price information and prepares it for display on a user interface, including an interface that visually presents the price information to the user in a grid or list format.

[1355] Specific examples

[1356] Below are some specific usage examples.

[1357] Example: Determining the buyback price of a trading card

[1358] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[1359] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[1360] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[1361] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[1362] 5. The device aggregates the extracted features and sends them to the server.

[1363] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[1364] 7. The server sends the acquired market price information to the terminal.

[1365] 8. The terminal displays the price information on the user interface and provides it to the user.

[1366] This flow allows users to quickly and easily check the buying price of trading cards and decide whether to sell them.

[1367] This system allows users to efficiently evaluate the value of multiple items at once, significantly reducing time and effort.

[1368] The processing flow will be explained below.

[1369] Step 1:

[1370] Users launch the app and use the camera to take a photo of the item they want to sell. They can also take a photo of multiple items at once.

[1371] Step 2:

[1372] The device receives the captured image and performs pre-processing such as noise removal, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[1373] Step 3:

[1374] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, which generates a bounding box to uniquely identify each object.

[1375] Step 4:

[1376] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[1377] Step 5:

[1378] The device compresses the extracted features into data packets and transmits them to a server over the Internet, using common communication protocols (e.g., HTTP or WebSocket) to ensure reliable and fast transmission.

[1379] Step 6:

[1380] The server analyzes the received feature data and searches the database. This search is performed by using the feature as a key to match the corresponding product information in the database.

[1381] Step 7:

[1382] The server retrieves market price information for the relevant items from its database and compiles it into a data packet, which identifies how much each item is worth.

[1383] Step 8:

[1384] The server sends the acquired market price information to the terminal, using a protocol (e.g., HTTPS) to ensure data integrity and communication stability.

[1385] Step 9:

[1386] The terminal analyzes the received market price information and prepares the data for display on the user interface, where the data is arranged in a format that is intuitively understandable to the user (for example, a grid or list format).

[1387] Step 10:

[1388] The terminal displays market price information on the user interface, allowing the user to visually confirm the value of each item.

[1389] Step 11:

[1390] The user can decide whether to sell or dispose of the item based on the displayed market price information, and can take further action (for example, proceed with the sale procedure) if necessary.

[1391] This detailed flow allows users to efficiently assess the value of items with minimal work and take appropriate action immediately.

[1392] Example 1

[1393] 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."

[1394] In the past, it was difficult to quickly and accurately assess the value of items, and users had to manually assess the value of each item, which required a significant amount of time and effort. Furthermore, manual assessments were inaccurate, making it difficult to obtain an accurate purchase price. To solve these problems, a system was needed that could take photos of items, automatically analyze the images, and display the purchase price.

[1395] 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.

[1396] In this invention, the server includes an imaging means for a user to capture images of multiple items, a preprocessing means for performing noise removal, image normalization, and light adjustment on the captured images, an identification means for individually identifying each item from the preprocessed images using an item detection algorithm and specifying the area, a feature extraction means for extracting character recognition, shape features, and color features as feature quantities of each identified item, a transmission means for transmitting the extracted feature quantities via the Internet, a search means for the server to search information in a database based on the feature quantities and obtain purchase price information, and a display means for receiving the obtained purchase price information and visually presenting it to the user, thereby enabling the user to quickly and accurately evaluate the value of the items.

[1397] "User" refers to the person who takes an image of an item of interest and inputs it into the system.

[1398] An "item" is an object whose value a user wishes to assess through the system.

[1399] "Photographing means" refers to a device such as a camera that a user uses to take an image of an item.

[1400] The "pre-processing means" refers to a means for performing noise removal, image normalization, and light adjustment on the captured image.

[1401] "Identification means" refers to a means for individually identifying each item and specifying its region from the preprocessed image using an item detection algorithm.

[1402] "Features" refers to information such as character recognition, shape features, and color features extracted from each identified item.

[1403] The "feature extraction means" refers to a means for extracting the feature amount of each identified item.

[1404] The "transmission means" refers to a means for transmitting the extracted feature amount to a server via the Internet.

[1405] The "search means" refers to a means for searching the database based on the feature amount received by the server and obtaining purchase price information.

[1406] "Database" refers to an information aggregation system that stores information on various items and purchase prices.

[1407] The "display means" refers to a means for visually presenting the acquired purchase price information to the user.

[1408] "Noise reduction" refers to a process for removing unnecessary noise from a captured image.

[1409] "Image normalization" refers to the process of adjusting the size and color tone of a captured image to a certain standard.

[1410] "Light adjustment" refers to the process of appropriately adjusting the brightness and contrast of a captured image.

[1411] "Item detection algorithm" refers to a computational method for detecting item regions in an image.

[1412] "Optical character recognition technology" refers to technology for extracting text information from images.

[1413] A "convolutional neural network" is a type of deep learning technology used in image analysis.

[1414] MODE FOR CARRYING OUT THE INVENTION

[1415] The present invention relates to a system that allows users to quickly and efficiently evaluate the value of an item. This system allows users to take a photo of the item, and AI automatically displays the market price for the item using the photo. The following describes in detail the embodiments of the present invention.

[1416] System Overview

[1417] The system consists of the following main components:

[1418] 1. A means for users to take pictures

[1419] 2. Pre-processing means for noise reduction, image normalization, and light adjustment of the captured images.

[1420] 3. Identification means for individually identifying each item and identifying its area using an item detection algorithm from the preprocessed image.

[1421] 4. Feature extraction means for extracting character recognition (OCR), shape features, and color features as features of each item

[1422] 5. A means for transmitting the extracted features to a server via the Internet

[1423] 6. A search method for searching the database based on the feature values ​​received by the server and obtaining purchase price information

[1424] 7. Display means for visually presenting the acquired price information to the user

[1425] Hardware and software used

[1426] This system uses the following hardware and software:

[1427] User device: smartphone or tablet

[1428] Camera module: A camera built into a smartphone or tablet

[1429] Pre-processing software: Image processing library (e.g., OpenCV)

[1430] Object detection algorithms: YOLO (You Only Look Once) and Faster R-CNN

[1431] Character Recognition Software (OCR): Tesseract OCR

[1432] Communication protocol: HTTP or WebSocket

[1433] Server: Cloud server, database management system (e.g. MySQL)

[1434] Display software: User interface library (e.g., React, Vue.js)

[1435] Specific examples of explanation

[1436] Next, a specific example of use is shown.

[1437] Example: Determining the buyback price of a trading card

[1438] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[1439] 2. The device receives the photo and performs preprocessing such as noise reduction and light adjustment using the OpenCV image processing library.

[1440] 3. The device uses an object detection algorithm (such as YOLO or Faster R-CNN) to identify the location of each card and extract each card individually.

[1441] 4. The terminal extracts features such as the card name and series number from each card using Tesseract OCR or similar.

[1442] 5. The device aggregates the extracted features and sends them to the server using HTTP or WebSocket.

[1443] 6. The server searches the internal database (MySQL) based on the features and obtains the market price for buying each card.

[1444] 7. The server sends the acquired market price information in JSON format to the terminal.

[1445] 8. The device displays the price information in a user interface (e.g., React or Vue.js) and provides it to the user.

[1446] Prompt Sentence Examples

[1447] "Users take photos of multiple trading cards with their smartphone camera."

[1448] "The device will remove noise from the captured image and adjust the lighting."

[1449] "The terminal uses an object detection algorithm to determine the location of each card."

[1450] The device uses Tesseract OCR to extract features and sends them to the server.

[1451] The server searches its internal database based on the features and obtains the purchase price information.

[1452] "The terminal displays information on a user interface and provides it to the user."

[1453] In this way, by using the system of the present invention, users can quickly and accurately evaluate the market price for buying an item.

[1454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1455] Step 1:

[1456] The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they wish to sell. The input is image data acquired through the camera, and the output is the captured image file. Specifically, the user operates the camera app to take a photo of an item (e.g., a trading card).

[1457] Step 2:

[1458] The device receives images taken by the user and performs preprocessing. The input is the captured image file, and the output is preprocessed image data. Preprocessing involves applying a noise reduction filter, normalizing the image, and adjusting the brightness. Specifically, noise reduction and brightness adjustment are performed automatically using an image processing library (e.g., OpenCV).

[1459] Step 3:

[1460] The device analyzes the preprocessed image and identifies the object using an object detection algorithm (e.g., YOLO or Faster R-CNN). The input is the preprocessed image data, and the output is the bounding box (area) of the object. Specifically, the device runs the algorithm to detect the object in the image and obtain its coordinate information.

[1461] Step 4:

[1462] The device extracts features from each identified object. The input is image data including the object's bounding box, and the output is the extracted features (e.g., character, shape, color). Feature extraction involves character recognition (OCR), shape analysis, and color feature extraction. Specifically, it uses Tesseract OCR to extract character information and executes a shape analysis algorithm.

[1463] Step 5:

[1464] The device collects the extracted features and sends them to a server via the Internet. The input is the feature data (e.g., in JSON format), and the output is the results sent to the server. Specifically, the data is sent to the server using HTTP or WebSocket protocol.

[1465] Step 6:

[1466] The server searches the database based on the received features. The input is the feature data sent from the device, and the output is the purchase price information as a search result. Specifically, it generates a database query and compares it with the internal item information.

[1467] Step 7:

[1468] The server obtains purchase price information and sends it to the terminal. The input is the purchase price information from the database, and the output is the price information (e.g., JSON format) sent to the terminal. Specifically, it formats the obtained price information and sends it to the terminal via the Internet.

[1469] Step 8:

[1470] The terminal analyzes the received price information and prepares it for display on the user interface. The input is the price information received from the server (e.g., JSON format), and the output is the visual information displayed on the user interface. Specifically, the price information is displayed in list format on the screen using React and Vue.js.

[1471] (Application example 1)

[1472] 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."

[1473] Conventional product purchase price appraisal systems have the drawback of requiring users to input product information individually, which is time-consuming and laborious. Furthermore, the lack of highly accurate product identification and feature extraction results inconsistent appraisal results. Furthermore, the user interface is not intuitive, making it difficult for users to use.

[1474] 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.

[1475] In this invention, the server includes a means for preprocessing images taken by a user and identifying the items, a means for extracting feature quantities of the items, and a means for searching a database to obtain buy price information. This allows for efficient preprocessing and identification even when a user takes photos of multiple items at once, making it possible to quickly provide accurate buy price information.

[1476] The "photography means" is a device or function for taking an image of an item that a user wishes to sell, and typically uses a camera built into a smart device.

[1477] The "pre-processing means" is a device or function that performs processes such as noise removal, image normalization, and lighting adjustment on the captured image.

[1478] "Identification means" is a device or function that uniquely identifies each item from the pre-processed image, typically using an object detection algorithm.

[1479] The "feature extraction means" is a device or function that extracts feature quantities from each identified item, and includes character recognition, shape features, color features, and the like.

[1480] The "transmission means" is a device or function for transmitting the extracted features to a server, and uses HTTP or WebSocket as a communication protocol.

[1481] The "search means" is a device or function that searches the database in the server and obtains the purchase price information of the corresponding item.

[1482] The "display means" is a device or function that displays the acquired purchase price information to the user, and provides the information visually via a user interface.

[1483] An "artificial intelligence model" is a trained algorithm or technology used for item identification or feature extraction, including YOLO and OCR.

[1484] "Smart device" is a general term for electronic devices such as mobile phones and tablet devices equipped with a camera function.

[1485] The system embodying the present invention allows a user to take an image of an item they wish to sell, and then quickly and efficiently evaluates the purchase price of the item based on the image. A specific embodiment of the system will be described below.

[1486] System Configuration

[1487] The system mainly consists of the following components:

[1488] 1. Photography Method:

[1489] It is a means for a user to take an image of an item, typically using a camera built into a smart device.

[1490] 2. Pretreatment methods:

[1491] It is a means of preprocessing the captured images such as noise removal, image normalization, lighting adjustment, etc. This is performed using the OpenCV library.

[1492] 3. Means of identification:

[1493] It is a means of identifying objects from preprocessed images and uses the YOLO (You Only Look Once) algorithm to detect objects.

[1494] 4. Feature extraction method:

[1495] This is a means of extracting features from identified items, including character recognition (OCR), shape features, color features, etc. Specifically, the Tesseract OCR engine is used.

[1496] 5. Means of transmission:

[1497] This is a means for sending extracted features to a server, and uses HTTP or WebSocket as the communication protocol.

[1498] 6. Search Methods:

[1499] This is a method of searching a database based on the feature values ​​received on the server side and obtaining the purchase price information for the corresponding item. The database can be, for example, PostgreSQL or MySQL.

[1500] 7. Display means:

[1501] This is a means of displaying the acquired purchase price information to the user, and provides the information visually through a user interface, using UI libraries such as React Native and Flutter.

[1502] Hardware and Software Configuration

[1503] Hardware:

[1504] Smart devices (mobile devices with camera functions and tablet devices)

[1505] Server (Cloud server or on-premise server)

[1506] software:

[1507] OpenCV (image preprocessing)

[1508] YOLO (article identification)

[1509] Tesseract OCR engine (feature extraction)

[1510] HTTP / WebSocket (communication protocol)

[1511] Database (PostgreSQL, MySQL, etc.)

[1512] React Native or Flutter (user interface)

[1513] Specific examples

[1514] Take the example of a user selling a smartphone that they no longer need.

[1515] 1. The user launches the app and takes a picture of the smartphone they want to sell.

[1516] 2. The device receives the image and uses OpenCV to remove noise and adjust the lighting.

[1517] 3. The device uses YOLO to identify the smartphone and uses the Tesseract OCR engine to extract features such as the model name and serial number.

[1518] 4. The device sends the features to the server via the HTTP protocol.

[1519] 5. The server searches the database based on the received features and obtains the purchase price information.

[1520] 6. The server sends the acquired data to the terminal.

[1521] 7. The terminal uses React Native or Flutter to display pricing information in the user interface.

[1522] Prompt Sentence Examples

[1523] As an example of how a user might give instructions to the system, the following prompt sentences might be used:

[1524] "Please take a photo on your smartphone and let us know the purchase price."

[1525] This system allows users to quickly and accurately find out the purchase price of their items, significantly reducing the amount of time and effort required.

[1526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1527] Step 1:

[1528] A user uses the camera on their smart device to take a picture of the item they want to sell (e.g., a smartphone, accessory, etc.). The input is the image of the item taken by the user, and the output is the captured image file.

[1529] Step 2:

[1530] The device receives the captured image and performs preprocessing. The input is the captured image file, and the output is the preprocessed image. This preprocessing includes specific operations such as noise removal, image normalization, and lighting adjustment using the OpenCV library.

[1531] Step 3:

[1532] The device analyzes the preprocessed image and identifies the object using the YOLO algorithm. The input is the preprocessed image, and the output is the object's location (bounding box). Specifically, the YOLO model identifies the object's area.

[1533] Step 4:

[1534] The device extracts features from the identified items. The input is an image with the item's location information, and the output is the extracted features (e.g., text information, color features, shape features). Specifically, the Tesseract OCR engine is used to extract the item's model name and serial number as text data.

[1535] Step 5:

[1536] The device sends the extracted features to the server using HTTP or WebSocket protocol. The input is the extracted feature data, and the output is a message that the transmission was successful. The specific operation includes encoding the features as parameters of an HTTP POST request and sending them over the network.

[1537] Step 6:

[1538] The server searches the database based on the received features and obtains the corresponding purchase price information. The input is feature data, and the output is purchase price information. Specific data processing involves using SQL queries to search for related information in the database and obtain the corresponding purchase price information.

[1539] Step 7:

[1540] The server sends the acquired purchase price information to the terminal. The input is the purchase price information, and the output is a message that transmission to the terminal was successful. Specific operations include formatting the price information as an HTTP response and sending it back over the network.

[1541] Step 8:

[1542] The terminal displays the received price information on a user interface. The input is the purchase price information received from the server, and the output is the price information visually displayed to the user. Specific operations include updating UI components using React Native or Flutter.

[1543] This series of processes allows the user to quickly find out the selling price of the item.

[1544] 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.

[1545] This invention combines an emotion engine with a system that allows users to quickly and efficiently evaluate the value of an item. Users take a photo, and the AI ​​not only automatically displays the market price for the item using that photo, but also recognizes the user's emotions and optimizes the evaluation process.

[1546] System Overview

[1547] The system consists of the following main components:

[1548] 1. A means for users to take pictures

[1549] 2. Preprocessing means for preprocessing the captured images

[1550] 3. A means of identifying each item individually from the preprocessed image.

[1551] 4. Feature extraction method to extract the features of each item

[1552] 5. A method for transmitting extracted features to the server

[1553] 6. A search method to search the database on the server and obtain purchase price information

[1554] 7. Display means for receiving the acquired price information and displaying it on the user interface

[1555] 8. Emotion engine that recognizes emotions by analyzing the user's facial expressions and voice

[1556] Program processing

[1557] 1. The user launches the app and uses the camera function of their smartphone or tablet to take a photo of the item they want to sell.

[1558] 2. The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization to improve the accuracy of image recognition.

[1559] 3. The device analyzes the preprocessed image and uses the discrimination means to identify individual items. It uses an item detection algorithm to locate each item and generate a bounding box.

[1560] 4. For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[1561] 5. The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP or WebSocket).

[1562] 6. The server searches the database based on the received feature values, matches the corresponding item information in the database using the feature values ​​as a key, and obtains the purchase price information.

[1563] 7. The server sends the acquired price information to the terminal, using an appropriate protocol to ensure data reliability and communication stability.

[1564] 8. The terminal parses the price information and prepares the data for display on the user interface in a visually friendly format.

[1565] 9. The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[1566] 10. The device will adapt the user interface based on the perceived emotion. For example, it could be designed to negotiate a price or offer additional services if it determines the user is dissatisfied.

[1567] Specific examples

[1568] Below are some specific usage examples.

[1569] Example: Determining the buyback price of trading cards and taking user sentiment into account

[1570] 1. The user arranges multiple trading cards from their home on a table and takes a photo using the app's camera function.

[1571] 2. The device receives the photo and performs pre-processing such as noise reduction and light adjustment.

[1572] 3. The device uses an object detection algorithm to determine the location of each card and separate them individually.

[1573] 4. The terminal extracts features such as the card name and series number from each card using OCR or other methods.

[1574] 5. The device sends the extracted features to the server.

[1575] 6. The server searches the database for information based on the features and obtains the market price for buying each card.

[1576] 7. The server sends the acquired market price information to the terminal.

[1577] 8. The terminal displays the price information on the user interface.

[1578] 9. The device captures the user's facial expressions with a camera and collects audio with a microphone, which is then analyzed by an emotion engine.

[1579] 10. If the device determines using its emotion engine that the user is not satisfied, it will make additional offers or negotiate.

[1580] This flow allows users to quickly and easily check the buying price of trading cards and take appropriate action based on their feelings. This system allows users to efficiently evaluate the value of items with minimal work and take appropriate action immediately.

[1581] The processing flow will be explained below.

[1582] Step 1:

[1583] Users launch the app and use the camera function to take a photo of the item they want to sell, using the camera on their smartphone or tablet.

[1584] Step 2:

[1585] The device receives the captured image and performs pre-processing such as noise reduction, light adjustment, and resolution optimization, thereby improving the accuracy of image recognition.

[1586] Step 3:

[1587] The device inputs the preprocessed images into an object detection algorithm (e.g., YOLO or Faster R-CNN) to determine the location of each object, generating a bounding box that identifies each object individually.

[1588] Step 4:

[1589] For each identified item, the device uses a feature extraction algorithm to extract features, including character recognition (OCR), shape features, and color features.

[1590] Step 5:

[1591] The device compresses the extracted features into data packets and sends them to a server over the Internet using a communication protocol (e.g., HTTP or WebSocket).

[1592] Step 6:

[1593] The server analyzes the received feature data and searches the database. Using the feature data as a key, it matches the corresponding item information in the database and obtains the purchase price information.

[1594] Step 7:

[1595] The server sends the acquired purchase price information to the terminal, using an appropriate protocol such as HTTPS to ensure data reliability and communication stability.

[1596] Step 8:

[1597] The terminal parses the received price information and prepares the data for display on the user interface in a visually friendly format.

[1598] Step 9:

[1599] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, which uses machine learning algorithms to recognize the user's emotions.

[1600] Step 10:

[1601] The device adjusts the user interface and display content based on the user's emotions recognized by the emotion engine. For example, if the device determines that the user is dissatisfied, it is designed to offer price negotiations or additional offers.

[1602] Step 11:

[1603] Users can then review the final displayed price information and any additional offers and decide whether to sell or dispose of the item. If necessary, they can proceed with the sale process within the app.

[1604] This system not only allows users to efficiently evaluate the value of an item by taking a photo and analyzing emotions once, but also allows them to receive the most appropriate service based on their emotions.

[1605] Example 2

[1606] 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."

[1607] In conventional purchase price evaluation systems, even if a user takes a picture of an item, the system does not accurately identify the item or extract its features, often resulting in an incorrect price evaluation. Furthermore, because the system does not take into account the user's feelings, it does not take appropriate action even if the user is dissatisfied. This leads to the problem of reduced user convenience and satisfaction.

[1608] 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: an imaging means for a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting feature values ​​for each identified item; a transmission means for transmitting the extracted feature values ​​to the server; a search means for the server to search for corresponding information in a database based on the feature values ​​and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition acquisition means for acquiring the user's facial expressions and voice to recognize emotions; and an interface adjustment means for adjusting the user interface based on the results of the emotion recognition. This makes it possible to accurately extract feature values ​​from images of items captured by the user, present an appropriate buy price, and adjust the interface according to the user's satisfaction.

[1609] "Photographing means" refers to a device or function that allows a user to take images of multiple items.

[1610] "Pre-processing means" refers to processing of captured images, such as noise removal, light adjustment, and resolution optimization.

[1611] "Identification means" refers to algorithms or functions that uniquely identify and locate each item in the pre-processed image.

[1612] "Feature extraction means" refers to technology or algorithms for extracting features such as character recognition, shape features, and color features of each identified item.

[1613] The "transmission means" refers to a device or communication protocol for transmitting the extracted feature quantity to a server.

[1614] "Search means" refers to a technology or system for searching for corresponding information in a database based on the features received by the server and obtaining purchase price information.

[1615] The "display means" refers to a device or interface for displaying the acquired purchase price information to the user.

[1616] "Acquisition means for emotion recognition" refers to devices or technologies for acquiring a user's facial expressions and voice and recognizing emotions.

[1617] "Interface adjustment means" refers to functions and technologies for adjusting the user interface based on the results of emotion recognition.

[1618] The present invention combines an emotion recognition engine with a system that allows users to quickly and efficiently evaluate the value of an item. Specific embodiments of this system will be described below.

[1619] System Overview

[1620] The present invention is a system that allows users to take images of multiple items using the camera on their smartphone or tablet, extracts features of the items based on the images, and displays the purchase price via a server.Furthermore, it has the feature of recognizing the user's emotions and adjusting the interface appropriately.

[1621] Hardware and software used

[1622] Image capture method: The built-in camera of a smartphone or tablet. This hardware is used to capture images of the item.

[1623] Pre-processing means: Image processing library (e.g. OpenCV). This software denoises the images, adjusts lighting, and optimizes resolution.

[1624] Identification method: Item detection algorithms (e.g., YOLO, SSD). These algorithms identify individual items from pre-processed images.

[1625] Feature extraction methods: OCR software and algorithms for extracting shape and color features. Used to extract the features of an object.

[1626] Transmission method: A communication protocol such as HTTP or WebSocket. The extracted features are sent to the server using this protocol.

[1627] Search method: Database search algorithm. The server searches for corresponding information in the database based on the features received and obtains the purchase price.

[1628] Display: The display device of a smartphone or tablet. This device is used to display the buyback price to the user.

[1629] Acquisition means for emotion recognition: Camera and microphone. These hardware capture the user's facial expressions and voice.

[1630] Emotion Recognition Engine: Machine learning algorithms (e.g., facial recognition, voice emotion identification technology) used to analyze user emotions.

[1631] Interface adjustment means: A software library for dynamically changing the user interface. The interface is adjusted based on the results of emotion recognition.

[1632] Specific examples

[1633] Below are some specific usage examples.

[1634] Example: Determining the buyback price of trading cards and taking user sentiment into account

[1635] 1. User: Arranges several trading cards from home on a table and takes a photo using the camera on their smartphone.

[1636] 2. Device: Receives the photo and uses an image processing library to perform preprocessing such as noise reduction and light adjustment.

[1637] 3. Terminal: Using an object detection algorithm, each card is located and individually cut out from the preprocessed image.

[1638] 4. Terminal: Using OCR software, extract features such as the card name and serial number from each card.

[1639] 5. Terminal: The extracted features are sent to the server using the HTTP protocol.

[1640] 6. Server: Search the database based on the features and obtain the market price for buying each card.

[1641] 7. Server: Sends the acquired market price information to the terminal using the HTTP protocol.

[1642] 8. Terminal: Display price information in a user interface.

[1643] 9. Device: Using a camera and microphone, the user's facial expressions and voice are captured and analyzed using an emotion recognition engine.

[1644] 10. Terminal: Based on the results of emotion recognition, if the user is not satisfied, the terminal will suggest additional offers or price negotiations.

[1645] Examples of prompts for generative AI models

[1646] Here are some example prompts to input to the generative AI model:

[1647] "Please describe in detail a system that takes a user's photo of a trading card, displays the market value of each card, and suggests additional offers based on the user's sentiment. Please also provide a detailed description of each processing step and its specific behavior."

[1648] This prompt allows the generative AI model to provide a detailed explanation of each processing step of the system and the specific actions that accompany it.

[1649] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1650] Step 1:

[1651] The user launches the app and uses the camera to take a photo of the item they want to sell.

[1652] Input: An image taken by a user using the camera on their smartphone or tablet.

[1653] How it works: The user launches the app, selects the camera function, points the camera at an object, and presses the shutter button.

[1654] Output: Image data of the item.

[1655] Step 2:

[1656] The device receives the captured images and performs pre-processing such as noise removal, light adjustment, and resolution optimization.

[1657] Input: Image data of photographed item.

[1658] What it does: It uses an image processing library (e.g. OpenCV) to remove noise from the image, adjust color and brightness, and optionally resize and optimize the resolution of the image.

[1659] Output: Preprocessed image data.

[1660] Step 3:

[1661] The terminal analyzes the pre-processed images and identifies individual items using the identification means.

[1662] Input: Preprocessed image data.

[1663] How it works: Uses an object detection algorithm (e.g., YOLO or SSD) to locate the object and generate a bounding box.

[1664] Output: Bounding box coordinate data for each object.

[1665] Step 4:

[1666] The terminal extracts features for each identified item using a feature extraction algorithm.

[1667] Input: Bounding box coordinate data and preprocessed image data for each object.

[1668] How it works: It uses OCR (Optical Character Recognition) software to extract text information from an object, as well as analyze features such as color and shape, and saves them as a digital signature.

[1669] Output: Feature data for each item.

[1670] Step 5:

[1671] The terminal compresses the extracted features into a data packet and transmits it to the server.

[1672] Input: Feature data for each item.

[1673] How it works: The feature data is efficiently compressed using a compression algorithm and sent to a server using the HTTP protocol.

[1674] Output: A data packet containing feature data.

[1675] Step 6:

[1676] The server searches the database based on the received features.

[1677] Input: A data packet containing the received feature data.

[1678] Operation: The server analyzes the features and uses the index to search for the corresponding item data in the database. The search result is the purchase price information.

[1679] Output: Purchase price information data for each item.

[1680] Step 7:

[1681] The server transmits the acquired price information to the terminal.

[1682] Input: Purchase price information data for each item.

[1683] How it works: The server reassembles the acquired price information into data packets and sends them to the device using the HTTP protocol, retransmitting and acknowledging them as necessary to ensure communication stability.

[1684] Output: A data packet containing the buy price information.

[1685] Step 8:

[1686] The terminal parses the price information and prepares the data for display on the user interface.

[1687] Input: A data packet containing the buy price information for each item.

[1688] What it does: Formats the retrieved pricing information based on your user interface design and displays it in a visually easy-to-understand format (e.g., graph or comparison table).

[1689] Output: Price display data on the user interface.

[1690] Step 9:

[1691] The device also uses a camera and microphone to capture the user's facial expressions and voice, which are then analyzed using an emotion engine.

[1692] Input: User's facial expression and voice data.

[1693] How it works: It acquires video data from the camera and audio data from the microphone, and uses emotion analysis algorithms (e.g., facial recognition and voice emotion identification technology) to recognize the user's emotions.

[1694] Output: User's emotional state data.

[1695] Step 10:

[1696] The device adjusts the user interface based on the recognized emotion.

[1697] Input: User emotional state data.

[1698] How it works: If the system determines that the user is dissatisfied, it automatically displays additional offers and price negotiation suggestions, and customizes interface elements and messages based on emotion recognition results.

[1699] Output: Adjusted user interface display data.

[1700] (Application example 2)

[1701] 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."

[1702] In conventional product value evaluation systems, when a user evaluates the value of an item, the system simply automatically displays the market price for the item, and does not take appropriate action that takes into account the user's feelings or satisfaction. As a result, if the user is dissatisfied with the system's display or wishes to negotiate further, the system is unable to respond, resulting in a poor user experience.

[1703] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an imaging means for allowing a user to capture images of multiple items; a preprocessing means for preprocessing the captured images; an identification means for individually identifying each item from the preprocessed images; a feature extraction means for extracting features of each identified item; a transmission means for transmitting the extracted features to the server; a search means for the server to search for corresponding information in a database based on the features and acquire buy price information; a display means for receiving the acquired buy price information and displaying it to the user; an emotion recognition means for capturing the user's facial expressions with a camera and collecting voice with a microphone to recognize emotions; and an adjustment means for adjusting the interface for the user based on the emotion recognized by the emotion recognition means. This makes it possible to automatically make additional offers or propose price negotiations even if the user is not satisfied with the offered price, thereby improving the user experience.

[1704] "Photographing means" refers to a device or function that allows a user to photograph an image of an item.

[1705] The "pre-processing means" is a function for performing pre-processing such as noise removal and light adjustment on the captured image.

[1706] "Identification means" refers to algorithms or functions that identify individual items from pre-processed images.

[1707] The "feature extraction means" is a function for extracting information such as character recognition, shape features, and color features from the identified article.

[1708] The "transmission means" is a communication means for transmitting the extracted feature amount to the server.

[1709] The "search means" is a function for searching the database based on the feature amount received by the server and acquiring corresponding information.

[1710] The "display means" is an interface or device for visually notifying the user of the acquired purchase price information.

[1711] The "emotion recognition means" is a function for recognizing emotions by analyzing the user's facial expressions and voice.

[1712] The "adjustment means" is a function for adjusting the user interface based on the emotion recognized by the emotion recognition means.

[1713] The present invention provides a system that allows users to quickly and efficiently evaluate the value of items, incorporating an emotion engine. Using photos taken by the user, AI automatically displays the market price for the item, and is also able to recognize the user's emotions and optimize the evaluation process. An embodiment of this system is shown below.

[1714] System configuration

[1715] 1. Photography: The user uses the camera on their smartphone or tablet to take a picture of the item.

[1716] 2. Pre-processing: The captured images are pre-processed using OpenCV to remove noise, adjust lighting, and optimize resolution.

[1717] 3. Identification: We use the TensorFlow Object Detection API to identify each object individually from the preprocessed image.

[1718] 4. Feature extraction: Using Tesseract OCR or custom CNN, features such as character recognition, shape features, and color features of the identified items are extracted.

[1719] 5. Transmission means: The extracted features are compressed into data packets and transmitted to the server using a communication protocol (e.g., HTTP / HTTPS).

[1720] 6. Search method: The server uses MongoDB to search for corresponding information in the database based on the received features and obtains the purchase price information.

[1721] 7. Display means: The acquired purchase price information is sent to the terminal and visually displayed on the user interface.

[1722] 8. Emotion recognition: Using Microsoft Azure Face API and Google Cloud Speech-to-Text, the user's facial expressions and voice are collected and emotions are recognized.

[1723] 9. Adjustment: Dynamically adjust the user interface based on emotions recognized by the emotion engine, such as negotiating prices or presenting additional offers.

[1724] Specific examples

[1725] Trading card valuation and emotional response

[1726] A user arranges multiple trading cards on a table at home and takes a photo with their smartphone camera. After taking the photo, the device preprocesses the image and identifies each card using an item detection algorithm. Next, Tesseract OCR extracts features such as the card name and series number, and sends the data packet to the server. The server uses MongoDB to search for the repurchase price information for the corresponding card and sends the information to the device. The device then displays the obtained price information on its user interface. At the same time, the device uses its camera and microphone to collect the user's facial expressions and voice, and recognizes their emotions using the Microsoft Azure Face API or Google Cloud Speech-to-Text. If the recognized emotion is determined to be "unhappy," the system immediately presents additional offers or price negotiations.

[1727] Prompt Sentence Examples

[1728] Example prompt for Tesseract OCR:

[1729] Recognize text from image: "Figure name", "Manufacturer", "Series number"

[1730] In this way, the system of the present invention can quickly and efficiently evaluate the value of an item to the user and provide appropriate actions that take emotions into consideration.

[1731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1732] Step 1:

[1733] The user launches the app and takes a photo of the item using the smartphone's camera. The image is then input into the device. The user follows the app's instructions to take a photo of the item under appropriate lighting.

[1734] Step 2:

[1735] The device receives the captured image and uses OpenCV to perform preprocessing such as noise removal, light adjustment, and resolution optimization. The input of the preprocessing is the captured image, and the output is the preprocessed image. Specifically, image filtering and contrast adjustment are performed.

[1736] Step 3:

[1737] The device analyzes the preprocessed image using the TensorFlow Object Detection API and uses a discriminator to identify individual objects. The input is the preprocessed image, and the output is a bounding box indicating the location of the object. The algorithm detects features in the image and identifies the area of ​​each object.

[1738] Step 4:

[1739] For each identified item, the device uses Tesseract OCR and a custom CNN to extract features such as character recognition, shape features, and color features. The input is the image region for each item, and the output is the extracted features. OCR reads the character information, and CNN analyzes the shape and color patterns.

[1740] Step 5:

[1741] The device compresses the extracted features into data packets and sends them to the server using a communication protocol (e.g., HTTP / HTTPS). The input is the features, and the output is the data packets sent to the server. This includes data compression and encryption.

[1742] Step 6:

[1743] The server uses MongoDB to search the database based on the received features and obtain the corresponding purchase price information. The input is the received features and the output is the obtained purchase price information. A database search algorithm is executed to extract the optimal price information.

[1744] Step 7:

[1745] The server sends the acquired purchase price information to the terminal. The input is the price information, and the output is the data sent to the terminal. Again, a communication protocol (e.g., HTTP / HTTPS) is used.

[1746] Step 8:

[1747] The terminal parses the received price information and prepares the data for visual display in the user interface. The input is the price information and the output is the information displayed to the user. It displays the information in a visually easy-to-understand graphical format.

[1748] Step 9:

[1749] The device uses a camera and microphone to collect the user's facial expressions and voice, and recognizes emotions using the Microsoft Azure Face API and Google Cloud Speech-to-Text. The input is a photo of the user's facial expression and voice, and the output is analyzed emotional information. The emotion recognition algorithm analyzes the data and identifies the user's emotion.

[1750] Step 10:

[1751] The device executes an adjustment method to adjust the user interface based on the recognized emotion. The input is emotion information, and the output is an adjusted user interface. Specifically, if satisfaction is low, price negotiation or additional offers are presented.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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).

[1759] 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.

[1760] 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."

[1761] 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.

[1762] 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).

[1763] 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.

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] The following is further disclosed regarding the above embodiment.

[1774] (Claim 1)

[1775] an image capture means for a user to capture images of a plurality of items;

[1776] a pre-processing means for performing pre-processing on the captured image;

[1777] an identification means for uniquely identifying each item from the preprocessed image;

[1778] a feature extraction means for extracting a feature amount of each of the identified articles;

[1779] a transmitting means for transmitting the extracted feature quantity to a server;

[1780] A search means for the server to search for corresponding information in a database based on the feature amount and acquire purchase price information;

[1781] a display means for receiving the acquired purchase price information and displaying it to a user;

[1782] A system including:

[1783] (Claim 2)

[1784] 2. The system according to claim 1, wherein the feature extraction means extracts character recognition, shape features, and color features.

[1785] (Claim 3)

[1786] 10. The system of claim 1, wherein the identifying means implements an item detection algorithm to identify the area of ​​the item.

[1787] "Example 1"

[1788] (Claim 1)

[1789] an image capture means for a user to capture images of a plurality of items;

[1790] a pre-processing means for performing noise removal, image normalization and light adjustment on the captured image;

[1791] an identification means for individually identifying each item and identifying its region from the preprocessed image using an item detection algorithm;

[1792] a feature extraction means for extracting character recognition, shape features, and color features as feature quantities of each of the identified articles;

[1793] a transmitting means for transmitting the extracted feature quantity via the Internet;

[1794] a search means for the server to search information in a database based on the feature amount and acquire purchase price information;

[1795] a display means for receiving the acquired purchase price information and visually presenting it to a user;

[1796] A system including:

[1797] (Claim 2)

[1798] 10. The system of claim 1, wherein the feature extraction means performs character recognition using optical character recognition techniques.

[1799] (Claim 3)

[1800] 2. The system of claim 1, wherein the identification means executes an item detection algorithm using a convolutional neural network to identify the area of ​​the item.

[1801] "Application Example 1"

[1802] (Claim 1)

[1803] an image capture means for a user to capture images of a plurality of items;

[1804] a pre-processing means for performing pre-processing on the captured image;

[1805] an identification means for uniquely identifying each item from the preprocessed image;

[1806] a feature extraction means for extracting a feature amount of each of the identified articles;

[1807] a transmitting means for transmitting the extracted feature quantity to a server;

[1808] A search means for the server to search for corresponding information in a database based on the feature amount and acquire purchase price information;

[1809] a display means for receiving the acquired purchase price information and displaying it to a user;

[1810] A photography method using a smart device,

[1811] means for identifying and characterizing the article using an artificial intelligence model;

[1812] A system including:

[1813] (Claim 2)

[1814] 2. The system according to claim 1, wherein the feature extraction means extracts character recognition, shape features, and color features, and the system displays database search results on a user interface.

[1815] (Claim 3)

[1816] 2. The system of claim 1, wherein the identifying means implements an object detection algorithm to identify the region of the item, and wherein the pre-processing means includes image denoising, normalization, and illumination adjustment.

[1817] "Example 2: Combining Emotion Engines"

[1818] (Claim 1)

[1819] an image capture means for a user to capture images of a plurality of items;

[1820] a pre-processing means for performing pre-processing on the captured image;

[1821] an identification means for uniquely identifying each item from the preprocessed image;

[1822] a feature extraction means for extracting a feature amount of each of the identified articles;

[1823] a transmitting means for transmitting the extracted feature quantity to a server;

[1824] A search means for the server to search for corresponding information in a database based on the feature amount and acquire purchase price information;

[1825] a display means for receiving the acquired purchase price information and displaying it to a user;

[1826] acquisition means for emotion recognition that acquires facial expressions and voices of a user and recognizes emotions;

[1827] an interface adjusting means for adjusting a user interface based on the result of the emotion recognition;

[1828] A system including:

[1829] (Claim 2)

[1830] 2. The system according to claim 1, wherein the feature extraction means extracts character recognition, shape features, and color features.

[1831] (Claim 3)

[1832] 10. The system of claim 1, wherein the identifying means implements an item detection algorithm to identify the area of ​​the item.

[1833] "Application example 2 when combining emotion engines"

[1834] (Claim 1)

[1835] an image capture means for a user to capture images of a plurality of items;

[1836] a pre-processing means for performing pre-processing on the captured image;

[1837] an identification means for uniquely identifying each item from the preprocessed image;

[1838] a feature extraction means for extracting a feature amount of each of the identified articles;

[1839] a transmitting means for transmitting the extracted feature quantity to a server;

[1840] A search means for the server to search for corresponding information in a database based on the feature amount and acquire purchase price information;

[1841] a display means for receiving the acquired purchase price information and displaying it to a user;

[1842] An emotion recognition means for capturing a user's facial expression with a camera and collecting their voice with a microphone to recognize their emotion;

[1843] an adjustment means for adjusting an interface for a user based on the emotion recognized by the emotion recognition means;

[1844] A system including:

[1845] (Claim 2)

[1846] 2. The system according to claim 1, wherein the feature extraction means extracts character recognition, shape features, and color features.

[1847] (Claim 3)

[1848] 10. The system of claim 1, wherein the identifying means implements an item detection algorithm to identify the area of ​​the item. [Explanation of symbols]

[1849] 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. an image capture means for a user to capture images of a plurality of items; a pre-processing means for performing pre-processing on the captured image; an identification means for uniquely identifying each item from the preprocessed image; a feature extraction means for extracting a feature amount of each of the identified articles; a transmitting means for transmitting the extracted feature quantity to a server; A search means for the server to search for corresponding information in a database based on the feature amount and acquire purchase price information; a display means for receiving the acquired purchase price information and displaying it to a user; A system including:

2. 2. The system according to claim 1, wherein said feature extraction means extracts character recognition, shape features, and color features.

3. 2. The system of claim 1, wherein said identifying means implements an item detection algorithm to identify the area of ​​the item.

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