System

Abstract

A system is provided.SOLUTION: A system, comprising: means for a user to input product information; means for transmitting the product information to a server; means for the server to store the product information in a database; means for the server to calculate an approximate purchase price based on the product information stored in the database; means for the server to recommend a purchase service specialized for a specific brand or category; means for the server to provide a method for disposing of a product that is not priced; means for transmitting a calculation result and recommendation information from the server to a user terminal; and means for the user terminal to display received information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When decluttering, consumers often have concerns about which buying service to use, what the approximate fair price would be, and the risk of trouble when using flea market sites. These anxieties make the decluttering process complicated and tedious, hindering smooth progress. The present invention aims to provide a system that allows consumers to easily obtain information about buyers and fair prices for unwanted items at home, and to provide a method for smoothly decluttering. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including the following means: a means for a user to input product information, a means for transmitting the product information to a server, a means for the server to store the product information in a database, a means for the server to calculate an approximate purchase price based on the product information stored in the database, a means for the server to recommend a purchase service specialized in a specific brand or category, a means for the server to provide a method for disposing of priceless products, a means for the server to transmit the calculation results and recommended information to a user terminal, and a means for the user terminal to display the received information to the user. This allows users to easily check product purchase prices and purchase destination information from the comfort of their own homes, making it possible to smoothly proceed with the decluttering process.

[0006] "User" refers to an individual or corporation that uses the system to input product information and check purchase prices and purchase destination information.

[0007] "Product information" refers to detailed information such as the brand name, category, purchase date, product condition, and images of the product you wish to sell.

[0008] A "terminal" is a device used by a user to input product information and communicate with the server, such as a smartphone, PC, or tablet.

[0009] "Server" refers to a computer system that receives product information sent by users, stores it in a database, and performs processes such as calculating purchase prices and recommending buyers.

[0010] A "database" refers to an information management system installed on a server that stores product information, past purchase price data, market trends, purchaser information, etc.

[0011] "Purchase price" refers to the approximate purchase price of the product calculated by the system.

[0012] "Recommendation" refers to the system's suggestions to users about suitable services and disposal methods for purchasing products.

[0013] "Buyback service" refers to a specialized business or online service that specializes in a particular brand or category and allows users to sell unwanted items.

[0014] "Disposal methods" refers to suggestions for appropriate disposal, recycling, or donation of items that do not have a purchase price.

[0015] "Calculation result" refers to the approximate purchase price calculated by the server.

[0016] "Recommended information" refers to information on where to buy and how to dispose of items that the system presents to users.

[0017] "Display" refers to the device visually presenting calculation results and recommended information to the user. [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 illustrating 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] The system of the present invention supports efficient decluttering by allowing users to easily obtain information on the purchase prices and buyers of unwanted items.

[0040] Overall system configuration

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

[0042] 1. User Interface (UI): The input form for users to enter product information.

[0043] 2. Server: A central control unit that receives input data from users, stores it in a database, and performs calculations and searches.

[0044] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0045] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0046] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0047] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0048] User inputs and submits product information

[0049] 1. The user enters product information

[0050] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[0051] Example: "Brand bag, purchased in 2018, good condition"

[0052] 2. The device sends product information to the server

[0053] The terminal transmits the collected product information to the server using a secure communication protocol.

[0054] Data storage and purchase price calculation

[0055] 3. The server saves the product information to a database

[0056] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[0057] 4. The purchase price calculation module calculates the purchase price

[0058] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0059] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[0060] Recommendations for buyers and disposal methods

[0061] 5. The buyer recommendation module recommends suitable buyers

[0062] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0063] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0064] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0065] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0066] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0067] Sending and displaying results

[0068] 7. The server sends the calculation results and recommendation information to the device.

[0069] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0070] 8. Display the information received by the device to the user

[0071] The terminal displays the received information, i.e., approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[0072] The system allows users to obtain specific information about unnecessary items in their homes and efficiently progress through the decluttering process.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0076] Step 2:

[0077] The terminal sends the product information entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[0078] Step 3:

[0079] The server stores the received product information in a database, carefully recording attributes such as the product's brand name, category, purchase date, and condition.

[0080] Step 4:

[0081] The server's purchase price calculation module starts processing based on the product information stored in the database. The server calculates an approximate purchase price by referencing past purchase price data and market trend data stored in the database.

[0082] Step 5:

[0083] The server's buyback recommendation module searches the database for buyback services that specialize in specific brands or categories based on product information, and then lists the most suitable buyback services from the search results.

[0084] Step 6:

[0085] If the product has no resale value, the server's disposal recommendation module will suggest disposal options and allow the user to receive information about nearby recycling centers or donation locations.

[0086] Step 7:

[0087] The server sends the purchase price calculation results and recommended buyer information or disposal method to the terminal using a real-time communication protocol.

[0088] Step 8:

[0089] The device displays the received information to the user, who can then decide on their next course of action based on the approximate purchase price, recommended buyers, or disposal methods displayed.

[0090] This process allows users to easily obtain information on the purchase price and buyers of unwanted items from the comfort of their own home, making decluttering a smooth process.

[0091] Example 1

[0092] 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."

[0093] In today's consumer society, many users are looking for efficient ways to dispose of unwanted items. However, it is difficult and time-consuming to find out the purchase price for each item, the best buyer, and the appropriate disposal method for priceless items. Therefore, there is a need for a way for users to easily obtain information on the purchase price and buyers of unwanted items, as well as the appropriate disposal method for priceless items.

[0094] 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.

[0095] In this invention, the server includes means for a user to input product information, means for transmitting the product information to the server, means for storing the product information in a database, means for calculating an approximate purchase price, means for recommending a purchase service specialized in a specific brand or category, means for providing a method for disposing of priceless products, means for transmitting the calculation results and the recommended information to a user terminal, means for displaying to the user the information received by the user terminal, means for transmitting and receiving the product information using a secure communication protocol, means for predicting a purchase price using a machine learning model, and means for acquiring recycling information using an external API. This allows users to easily find out the specific purchase prices and purchase locations for unwanted products, as well as appropriate disposal methods, from the comfort of their own home.

[0096] "User" refers to an individual or organization that uses the system to input product information and obtain purchase prices and purchase destination information.

[0097] "Product Information" means detailed data about a product provided by a user, such as the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0098] The "server" is a computer system that serves as the core of the entire system, receiving and processing product information, calculating purchase prices, recommending buyers, and providing disposal methods.

[0099] A "database" is an information management system that allows a server to store and manage product information, past purchase price data, market trends, purchaser information, and so on.

[0100] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on the product information stored in the database.

[0101] The "Buyback Recommendation Module" is an algorithm or program that recommends buyback services that specialize in specific brands or categories based on product information.

[0102] A "disposal recommendation module" is an algorithm or program that provides information on appropriate disposal, recycling, or donation methods for items that have no price tag.

[0103] A "terminal" is a device such as a smartphone or PC that a user uses to input product information and receive and display calculation results and recommendation information from the server.

[0104] A "secure communication protocol" is a means of securely communicating data between a terminal and a server, and generally includes HTTPS.

[0105] A "machine learning model" is an artificial intelligence algorithm that predicts the purchase price of a product based on past data in a database and market trends.

[0106] "External API" means an external program interface used to obtain recycling information and other related information.

[0107] "Purchase price" is the estimated value of the unwanted item calculated by the purchase price calculation module of the server.

[0108] "Recommended information" refers to information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module of the server.

[0109] This invention relates to a system that allows users to easily obtain information on the purchase price and buyers of unwanted items. The main components of this system are a user interface, a server, a database, a purchase price calculation module, a buyer recommendation module, and a disposal method recommendation module.

[0110] First, the user enters product information using a dedicated application or website. The device used is a smartphone or PC, and the information is entered using this device. The information entered by the user includes the product brand name, category, purchase date, product condition, and, if necessary, an image. For example, the user might enter "brand bag, purchased in 2018, condition: good."

[0111] Next, the device sends the product information to the server. This communication uses a secure communication protocol (such as HTTPS). The communication module in the device calls the API endpoint and sends an HTTP request containing the product information to the server.

[0112] The server stores the received product information in a database. Databases such as MySQL or PostgreSQL are used as the database. A database management program on the server executes an "INSERT INTO" query to store the product information. Based on the stored product information, the server's purchase price calculation module uses a machine learning model (such as TensorFlow) to refer to past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0113] Next, the server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. A Flask application is used to search for buy-back stores, and the obtained buy-back store information is organized and formatted for display. For example, it might say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0114] Furthermore, if the item has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation. To do this, the Node.js program uses an external API to obtain recycling information and compares it with information stored in the database to determine the optimal disposal method. For example, it might say, "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0115] The server then sends the calculated purchase price and recommended buyer information to the device, which then displays this information to the user. Client-side scripts such as JavaScript are used to parse the received data and dynamically update the information in HTML or in the display area within the app. This allows the user to obtain specific information about the purchase price, buyer, and disposal method, and decide on their next course of action.

[0116] As a concrete example, the prompt sentence is shown below.

[0117] Example prompt sentence:

[0118] "Please tell me the details of the program that calculates the purchase price."

[0119] "Please explain the algorithm that recommends the best buyers."

[0120] "What are the steps for recycling products and recommending donation destinations?"

[0121] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user enters product information.

[0125] The user enters the product brand name, category, purchase date, product condition, and, if necessary, an image into an input form on a dedicated application or website. The entered information is internally converted into JSON or form data format. For example, the user enters product information such as "Brand bag, purchased in 2018, condition: good" and clicks the "Submit" button. Input: Detailed product information. Output: Aggregation of input data.

[0126] Step 2:

[0127] The terminal transmits the product information to the server.

[0128] The device sends the stored product information to the server using the HTTPS protocol. The communication module calls the API endpoint and sends a POST request. Input: Product information stored in the device. Output: Request to the server. Specifically, the data is encrypted and divided into packets before being sent.

[0129] Step 3:

[0130] The server stores the product information in a database.

[0131] The server analyzes the product information received from the terminal and saves it in a database. MySQL or PostgreSQL is used for the database. The database management program on the server executes an "INSERT INTO" query to save the product information. Input: Received product information. Output: Results saved in the database. Specifically, data validation is performed to confirm consistency.

[0132] Step 4:

[0133] The purchase price calculation module calculates the purchase price.

[0134] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database. Input data includes past purchase price data for similar products and market trends. A TensorFlow model is used to predict the price, and the results are saved in a temporary data area. Input: Product information in the database. Output: Estimated purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0135] Step 5:

[0136] The buyer recommendation module recommends suitable buyers.

[0137] The server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. The Flask application executes the search query and organizes and formats the buy-back store information obtained. Input: Buy-back store information in the database. Output: Recommended buy-back stores. For example, it would say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best choices."

[0138] Step 6:

[0139] The disposal method recommendation module suggests an appropriate disposal method.

[0140] If the product has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation destinations. The Node.js program retrieves recycling information from an external API and selects the optimal disposal method. Input: Product worthlessness information and recycling API information. Output: Recommended disposal method. A specific example would be, "This product is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0141] Step 7:

[0142] The server sends the calculation results and recommendation information to the terminal.

[0143] The server sends the calculated purchase price, recommended buyer information, and disposal method to the terminal. Data is again sent securely using the HTTPS protocol. Input: Calculation results and recommended information. Output: Data sent to the terminal. The specific operation is to generate an HTTP response.

[0144] Step 8:

[0145] The device displays the received information to the user.

[0146] The device analyzes the received information and displays it to the user. A client-side script such as JavaScript or React parses the data and dynamically displays it on the screen. Input: Data received from the server. Output: Displayed on the user interface. For example, the information displayed might be "Purchase price: 30,000 yen, Recommended purchase locations: Specialized purchase store A, Online purchase service B."

[0147] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[0148] (Application example 1)

[0149] 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."

[0150] Conventional systems for buying unwanted items have the problem that users have to input product information themselves, and then use that information to search for the appropriate buyer, which is time-consuming. Additionally, in certain physical stores, it can be difficult for staff to quickly calculate product purchase prices and provide appropriate recommendations to customers. This makes it difficult to achieve efficient decluttering, and further improvements in convenience are needed.

[0151] 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.

[0152] In this invention, the server includes: means for a user to input product information; means for transmitting the product information to the server; means for the server to store the product information in a database; means for the server to calculate an approximate purchase price based on the product information stored in the database; means for the server to recommend a purchase service specialized in a specific brand or category; means for the server to provide a disposal method for priceless products; means for transmitting the calculation results and recommended information from the server to a user terminal; means for the user terminal to display the received information to the user; means including a terminal for a physical store where customers or staff enter product information; means for inputting a prompt sentence into a generative AI model; and means for obtaining a response from the generative AI model based on the prompt sentence. This automates the entire process from inputting product information to calculating a purchase price and recommending an appropriate buyer, significantly reducing the workload of users and staff and enabling efficient decluttering.

[0153] "Means for users to input product information" refers to interfaces and devices (kiosks, tablets, smart glasses, etc.) that allow customers or staff to input data about products (such as brand name, category, date of purchase, product condition, and images).

[0154] The "means for transmitting the product information to the server" refers to a function and program for transmitting the product information entered by the customer or staff member to the server using a secure communication protocol.

[0155] The "means for the server to store the product information in a database" refers to the processes and techniques for storing the product information received by the server in a database and maintaining consistency and completeness.

[0156] "Means for the server to calculate an approximate purchase price based on the product information stored in the database" refers to the function by which the server applies an algorithm based on the information in the database and market trends to calculate an estimated purchase price for the product.

[0157] "Means for the server to recommend buy-back services specialized in specific brands or categories" refers to a function that searches the database for buy-back services corresponding to specific brands or categories and suggests the most suitable buy-back service to the user.

[0158] "Means for the server to provide methods for disposing of priceless items" refers to a function that provides users with information on appropriate disposal methods, recycling, and donation destinations for items that are not eligible for purchase.

[0159] "Means for transmitting calculation results and recommendation information from the server to the user terminal" refers to a function for transmitting the purchase price and recommendation information calculated by the server to the terminal used by the user via a secure communication protocol.

[0160] "Means for displaying to the user the information received by the user terminal" refers to an interface and program for displaying to the user the purchase price and recommendation information received by the user terminal in an easy-to-see manner.

[0161] "Means including terminals for physical stores where customers or staff can input product information" refers to devices and functions that allow customers to input and submit product information, such as kiosk terminals installed in physical stores or mobile devices used by staff.

[0162] "Means for inputting prompt text into the generative AI model" refers to the interface and functions that allow users and staff to input product information and instructions as text into the generative AI model.

[0163] "Means for obtaining a response from the generative AI model based on the prompt sentence" refers to a function that enables the server to obtain a response generated in response to a prompt input to the generative AI model.

[0164] The system of the present invention is designed to automate and streamline the process of buying unwanted items in brick-and-mortar stores. The basic components are a user interface (UI), a server, a database (DB), a buyback price calculation module, a buyback recommendation module, a disposal method recommendation module, and a generative AI model.

[0165] User inputs and submits product information

[0166] 1. The user enters product information

[0167] Using devices such as kiosks in physical stores or tablets or smart glasses used by staff, users or staff enter product information, including the product brand name, category, purchase date, product condition, and images (if applicable).

[0168] 2. The device sends product information to the server

[0169] The terminal transmits the collected product information to the server using a secure communication protocol (e.g., HTTPS).

[0170] Data storage and purchase price calculation

[0171] 3. The server saves the product information to a database

[0172] Once the server receives the product information, it stores it in a database (e.g., MySQL, PostgreSQL). This process involves standard database operations to ensure data integrity.

[0173] 4. The purchase price calculation module calculates the purchase price

[0174] The purchase price calculation module on the server calculates an approximate purchase price based on product information stored in the database, referring to past purchase price data for similar products and market trends. This module is implemented in a programming language such as Python.

[0175] Recommendations for buyers and disposal methods

[0176] 5. The buyer recommendation module recommends suitable buyers

[0177] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0178] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0179] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0180] Using generative AI models

[0181] 7. Enter a prompt into the generative AI model

[0182] The server generates a prompt sentence based on the input product information and inputs it into a generative AI model (e.g., GPT-3).

[0183] 8. Obtaining responses from generative AI models

[0184] The response generated by the generative AI model is obtained, and the final purchase price and recommendation information are determined based on that content.

[0185] Sending and displaying results

[0186] 9. The server sends the calculation results and recommendation information to the device.

[0187] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0188] 10. Display the information received by the device to the user

[0189] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[0190] Adding specific examples

[0191] For example, if a customer enters product information such as "Brand bag, purchased in 2018, condition: good," the following prompt sentence is input into the generative AI model.

[0192] Example prompt sentence:

[0193] This is a designer bag, purchased in 2018, in good condition. Please let me know the purchase price based on market trends. Also, please recommend any stores or online services that will buy this item.

[0194] Based on this prompt, the generative AI model calculates the purchase price and recommends an appropriate buyer, and the information obtained is sent back to the server and ultimately displayed on the user's or staff's device.

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] A user or staff member enters product information.

[0198] The terminal provides a function for users or staff to input data about products (brand name, category, purchase date, product condition, image, etc.) through an interface.

[0199] Input: Product information (brand name, category, purchase date, condition, image)

[0200] Output: Input product information data

[0201] Step 2:

[0202] Send product information to the server.

[0203] The terminal transmits the input product information to the server using a secure protocol (for example, HTTPS).

[0204] Input: Entered product information data

[0205] Output: Product information sent to the server

[0206] Step 3:

[0207] Store product information in a database.

[0208] The server stores the received product information in a database (MySQL or PostgreSQL), performing appropriate database operations to maintain data integrity and consistency.

[0209] Input: Product information sent to the server

[0210] Output: Product information stored in the database

[0211] Step 4:

[0212] Calculate the purchase price.

[0213] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database, market trends, and past purchase price data.

[0214] Input: Product information stored in the database, past purchase price data, market trend data

[0215] Output: Approximate purchase price

[0216] Step 5:

[0217] Recommend suitable buyers.

[0218] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0219] Input: Product information, purchase price, and purchase service data stored in the database

[0220] Output: Recommended buyer information

[0221] Step 6:

[0222] Propose appropriate disposal methods.

[0223] The server's disposal recommendation module provides users with information on appropriate disposal, recycling, and donation options for items that have no value or cannot be sold.

[0224] Input: Product information stored in the database

[0225] Output: Recommended disposal, recycling, or donation information

[0226] Step 7:

[0227] Input a prompt sentence into the generative AI model.

[0228] The server generates a prompt sentence based on the input product information and inputs it into the generative AI model.

[0229] Input: Product information, prompt text

[0230] Output: The prompt sent to the generative AI model

[0231] Step 8:

[0232] Obtain a response from the generative AI model.

[0233] The server obtains the response generated by the generative AI model and determines the final purchase price and recommendation information based on the content.

[0234] Input: Response from a generative AI model

[0235] Output: Final purchase price, recommendation information

[0236] Step 9:

[0237] The calculation results and recommendation information are transmitted to the terminal.

[0238] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0239] Input: Final purchase price, recommendation information

[0240] Output: Buy price and recommendation information sent to the terminal

[0241] Step 10:

[0242] Display the received information to the user.

[0243] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[0244] Input: Purchase price and recommendation information sent to the terminal

[0245] Output: Buy price and recommendation displayed to user

[0246] 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.

[0247] The system of the present invention not only allows users to easily obtain information on the purchase price and buyers of unwanted items, but also provides more appropriate information and optimizes the interface by taking into account the user's emotional state.

[0248] Overall system configuration

[0249] The system consists of the following main components:

[0250] 1. User Interface (UI): The input form for users to enter product information.

[0251] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[0252] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0253] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0254] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0255] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0256] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[0257] User inputs and submits product information

[0258] 1. The user enters product information

[0259] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[0260] Example: "Brand bag, purchased in 2018, good condition"

[0261] 2. The device sends product information to the server

[0262] The terminal sends the product information entered by the user to the server using a secure communication protocol.

[0263] Data storage and purchase price calculation

[0264] 3. The server saves the product information to a database

[0265] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[0266] 4. The purchase price calculation module calculates the purchase price

[0267] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0268] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[0269] Recommendations for buyers and disposal methods

[0270] 5. The buyer recommendation module recommends suitable buyers

[0271] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0272] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0273] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0274] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0275] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0276] Emotion Recognition and Interface Optimization

[0277] 7. Emotion engine recognizes the user's emotional state

[0278] The emotion engine analyzes the user's emotional state based on their operation and input, including the speed of their operation, frequency of their input, and text mining of their input.

[0279] Example: If a user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly.

[0280] 8. Emotional engine optimizes the interface

[0281] Based on the user's emotional state, the emotion engine changes the interface's appearance and operation, for example, providing more detailed instructions and reducing the number of steps required if the user is feeling anxious.

[0282] Sending and displaying results

[0283] 9. The server sends the calculation results and recommendation information to the device.

[0284] The server transmits the calculated purchase price, recommended purchaser information, or disposal method to the terminal.

[0285] 10. Display the information received by the device to the user

[0286] The terminal displays the received information, such as the approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[0287] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support tailored to their emotional state.

[0288] The processing flow will be explained below.

[0289] Step 1:

[0290] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0291] Step 2:

[0292] The terminal transmits the product information entered by the user to the server using a secure communication protocol.

[0293] Step 3:

[0294] The server receives the product information and stores it in a database, properly recording information such as the product's brand name, category, purchase date, and condition.

[0295] Step 4:

[0296] The server's purchase price calculation module calculates the purchase price based on the product information stored in the database, referring to past purchase price data and market trends to calculate an approximate purchase price.

[0297] Step 5:

[0298] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and selects the most suitable buyer.

[0299] Step 6:

[0300] If the product has no value or no price, the server's disposal recommendation module searches for information on the appropriate disposal method, recycling, or donation destination.

[0301] Step 7:

[0302] The emotion engine analyzes the user's input and operational status to recognize their emotional state. For example, if the user is typing quickly, it will determine that they are feeling impatient.

[0303] Step 8:

[0304] An emotion engine dynamically changes the interface based on the user's perceived emotional state, for example simplifying instructions or using more friendly language for an impatient user.

[0305] Step 9:

[0306] The server then sends the calculated purchase price and recommended buyer information or disposal method to the terminal. This transmission is also carried out using a secure communication protocol.

[0307] Step 10:

[0308] The device displays the received information to the user, including an approximate purchase price, recommended buyers, or disposal methods, allowing the user to decide on the next course of action.

[0309] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and with the support of an emotion engine, they can receive the most appropriate information tailored to their situation.

[0310] Example 2

[0311] 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."

[0312] In conventional buying and selling systems, users can input product information and obtain information on buying prices and buyers, but the system does not take into account the user's emotional state when providing information or optimizing the interface, which can be stressful for users. Furthermore, the system also faces issues such as inefficiency and inaccuracy in calculating product buying prices and recommending buyers.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0314] In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing the interface based on the emotional state, means for calculating an approximate purchase price based on product information, means for recommending a purchase service specialized for a specific brand or category, means for providing a method for disposing of priceless products, and means for transmitting the calculation results and recommendation information from the server to the user terminal. This allows the user to reduce stress while efficiently and accurately calculating the purchase price and receiving recommendations for buyers.

[0315] "User" refers to an individual or corporation that uses the system to request information on purchase prices and disposal methods for unwanted items.

[0316] "Product information" refers to detailed information about the unwanted product entered by the user, such as the brand name, category, purchase date, product condition, and image.

[0317] The "server" is a central control device that receives product information sent by users, processes it, and sends the results to the user terminal.

[0318] A "database" is an information management system that stores and manages data such as product information, past purchase price data, purchaser information, and market trends.

[0319] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on product information stored in a database, by referring to past purchase price data and market trends.

[0320] The "buyer recommendation module" is an algorithm or program that searches a database for the most suitable buyer based on product information and recommends it to the user.

[0321] A "disposal recommendation module" is an algorithm or program that provides users with information on appropriate disposal, recycling, or donation options for items that have no value.

[0322] An "emotion engine" is a program that analyzes the user's operation speed and input content to recognize their emotional state and reflect this in the system's operation.

[0323] A "user terminal" is a device (such as a smartphone or PC) that a user uses to input product information and receive and display the results from the server.

[0324] A "secure communication protocol" is a communication method that ensures security when sending and receiving data, such as HTTPS.

[0325] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides appropriate information and optimizes the interface by taking into account the user's emotional state.

[0326] Overall system configuration

[0327] The system consists of the following main components:

[0328] 1. User Interface (UI): The input form for users to enter product information.

[0329] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[0330] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0331] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0332] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0333] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0334] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[0335] 8. User terminal: A device (e.g., a smartphone or PC) on which a user enters product information and receives and displays the results from the server.

[0336] User inputs and submits product information

[0337] The user uses a device to input product information into an input form on a dedicated application or website. This information includes the product brand name, category, purchase date, product condition, and an image (if necessary). For example, the user can input information such as "Brand bag, purchased in 2018, condition: good." The input product information is sent from the device to the server using a secure communication protocol (e.g., HTTPS).

[0338] Data storage and purchase price calculation

[0339] The server stores the received product information in a database. This storage process involves standard database operations (e.g., SQL queries) to maintain data integrity. Based on the stored product information, a purchase price calculation module calculates the purchase price. This module references past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, the calculation might be "brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0340] Recommendations for buyers and disposal methods

[0341] The buy-back recommendation module can search a database for the most suitable buy-back store based on product information and recommend it to the user. For example, it might recommend, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best." Furthermore, for items that have no value, the disposal recommendation module will suggest the appropriate disposal method to the user. For example, it might suggest, "This item is not eligible for purchase. We recommend that you take it to a recycling center or donate it to 'NPO C.'"

[0342] Emotion Recognition and Interface Optimization

[0343] The emotion engine analyzes the user's speed and input to recognize their emotional state. This analysis uses text mining and natural language processing (NLP). For example, if the user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly. Based on the emotional state, the emotion engine can change the UI layout to optimize the user experience.

[0344] Sending and displaying results

[0345] The server sends the calculated purchase price, recommended buyer information, and disposal method to the device, which then displays the received information to the user, allowing the user to decide on their next action based on the information obtained.

[0346] Example: A user enters "brand bag, purchased in 2018, condition: good," and the device sends this to the server. The server uses a purchase price calculation module to calculate an approximate price, and displays "The purchase price for this bag is 30,000 yen" on the device. The device also displays recommendation information such as "The best places for this brand bag are 'Specialized Purchase Store A' and 'Online Purchase Service B'."

[0347] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support based on their emotional state.

[0348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0349] Step 1:

[0350] User enters product information

[0351] Users use their devices to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and an image (if necessary).

[0352] Input: Manual input by the user

[0353] Output: Product information temporarily saved on the device

[0354] Specific action: For example, the user enters "brand bag, purchased in 2018, condition: good."

[0355] Step 2:

[0356] The device sends product information to the server

[0357] The terminal sends the entered product information to the server using a secure communication protocol (e.g., HTTPS).

[0358] Input: User-entered data

[0359] Output: Product information that arrives at the server

[0360] Specific operation: The terminal encrypts the product information using SSL / TLS and sends it to the server.

[0361] Step 3:

[0362] The server saves the product information to a database

[0363] The server stores the received product information in a database, which involves standard database operations (e.g., SQL queries) to ensure data integrity.

[0364] Input: Product information that arrives at the server

[0365] Output: Product information stored in a database

[0366] Specific behavior: Executes an SQL query to insert product information into the database. "INSERT INTO ProductInfo (BrandName, Category, PurchaseDate, Condition, Image) VALUES ('Brand Bag', 'Bag', '2018-01-01', 'Good', 'image_path');"

[0367] Step 4:

[0368] The purchase price calculation module calculates the purchase price

[0369] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0370] Input: Product information stored in the database

[0371] Output: Calculated approximate purchase price

[0372] How it works: The buyback price calculation algorithm compares past buyback price data and market trend data, and then uses statistical analysis and machine learning models to predict prices. "It uses product information as model input and outputs an estimated price."

[0373] Step 5:

[0374] The buyer recommendation module recommends suitable buyers

[0375] The server's buyer recommendation module searches a database for buying services that specialize in the product brand or category, and recommends the most suitable buyer to the user.

[0376] Input: Product information stored in the database

[0377] Output: Recommended buyer list

[0378] Specific behavior: Executes an SQL query to search for buyback services for the relevant brand and category, and generates a list of the best buyback services. "SELECT FROM BuybackServices WHERE Category = 'Bags' AND Brand = 'Brand Bags';"

[0379] Step 6:

[0380] The disposal method recommendation module suggests appropriate disposal methods.

[0381] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0382] Input: Product information stored in the database

[0383] Output: List of recommended disposal methods

[0384] Specific behavior: Runs an SQL query to find and generate a list of disposal methods for the given category: SELECT FROM DisposalMethods WHERE Category = 'Bag';

[0385] Step 7:

[0386] Emotion engine recognizes the user's emotional state

[0387] The emotion engine analyzes the user's emotional state based on their operation and input, including operation speed, input frequency, and text mining of input content.

[0388] Input: User operation data and input data

[0389] Output: Perceived emotional state

[0390] Specific operation: Text mining is performed on the user's operation speed and input content, and the input is input into the emotion analysis model to output the emotion. "emotion_result = emotion_analysis_module.analyze(user_input)"

[0391] Step 8:

[0392] Emotional engine optimizes the interface

[0393] The emotion engine changes the look and feel of the interface based on the user's emotional state, for example by providing more detailed instructions or reducing the number of steps required.

[0394] Input: Perceived emotional state

[0395] Output: Optimized interface

[0396] What it does: The emotion engine dynamically changes UI components to display the appropriate message and layout for the user. "Sorry for the wait. I'll make this as easy as possible."

[0397] Step 9:

[0398] The server sends the calculation results and recommendation information to the device.

[0399] The server sends the calculated purchase price, recommended buyer information, and disposal method to the user's terminal.

[0400] Input: Purchase price, purchase destination list, disposal method list

[0401] Output: Calculation results and recommendations sent to the device

[0402] Specific operation: The server structures the result data through the API and sends it to the terminal. "The JSON format data is sent to the terminal via the API."

[0403] Step 10:

[0404] Displaying information received by the device to the user

[0405] The device displays the received information to the user in a way that is easy for the user to understand.

[0406] Input: Calculation results and recommendations received from the server

[0407] Output: Information displayed to the user

[0408] Specific operation: The device displays the purchase price and purchase destination information in a table format on the UI. "Purchase price: 30,000 yen Recommended purchase destinations: Specialized purchase store A, online purchase service B Disposal method: Recycling center D, donation to NPO C"

[0409] (Application example 2)

[0410] 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."

[0411] In conventional buy-back service systems, users often find it difficult to obtain information on the buy-back price and buyers of unwanted items, and the information provided does not reflect the user's emotional state, leaving many users dissatisfied. Furthermore, the lack of interface optimization that takes into account the user's emotional state results in a poor user experience.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and optimizing the interface, means for the user to input product information, means for transmitting the product information to the server, means for the server to store the product information in a database, means for the server to calculate an approximate purchase price based on the product information stored in the database, means for the server to recommend a purchase service specialized in a specific brand or category, means for the server to provide a method for disposing of priceless products, means for transmitting the calculation results and recommended information from the server to a user terminal, and means for the user terminal to display the received information to the user. This enables users to easily obtain purchase prices and purchase destination information for unwanted products and to receive optimal support tailored to their emotional state.

[0413] "User interface" refers to the screen layout and operating means that allow users to input product information, etc.

[0414] "Server" means a central control device that receives, stores, processes data and provides information to users.

[0415] A "database" is an information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0416] A "purchase price calculation module" is software that includes an algorithm that calculates an approximate purchase price based on stored product information.

[0417] A "buyer recommendation module" is software that includes an algorithm that recommends appropriate buyers based on product information.

[0418] The "disposal recommendation module" is software that includes an algorithm that suggests appropriate disposal, recycling, or donation options for items that have no price tag.

[0419] An "emotion engine" is software that recognizes the user's emotional state and reflects it in the operation of the entire system.

[0420] "Emotional state" refers to the user's current psychological state, analyzed based on factors such as the user's operation speed, input frequency, and text mining of input content.

[0421] "Interface optimization" refers to changing the appearance and operation of an interface depending on the user's emotional state.

[0422] A "user terminal" is a device operated by a user, such as a smartphone.

[0423] "Product information" refers to data entered by the user, such as the product brand name, category, purchase date, product condition, and images.

[0424] The "calculation result" refers to the approximate purchase price calculated by the purchase price calculation module.

[0425] "Recommendation information" is information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module.

[0426] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides optimal support according to the user's emotional state. The overall configuration of this system is as follows.

[0427] Overall system configuration

[0428] 1. User Interface (UI)

[0429] Users use a smartphone application to input product information, which has a function to scan barcodes and QR codes and automatically input the scanned product information.

[0430] 2. Emotion Engine

[0431] The system analyzes the user's emotional state based on their input speed and operation method. It uses a generative AI model for emotion analysis and determines the user's emotional state through text mining of the input content.

[0432] 3. Server

[0433] Product information storage: Product information sent by users is saved on the server and stored in a database. Product information includes brand name, category, purchase date, product condition, images, etc.

[0434] Calculation of purchase price: The purchase price calculation module on the server calculates the purchase price based on the product information stored in the database. The calculation is performed based on past purchase price data for similar products and market trends.

[0435] Buyer recommendation: The buy-back recommendation module on the server searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back service to the user.

[0436] Providing disposal methods: For items that are not eligible for purchase, a disposal recommendation module on the server will suggest recycling methods or donation destinations to users.

[0437] 4. User Device

[0438] The system receives calculation results and recommendation information sent from the server and displays them to the user. The interface is also optimized according to the user's emotional state. For example, if the user is feeling anxious, the system will display more detailed explanations to reassure the user.

[0439] Hardware and software used

[0440] Hardware

[0441] Smartphone camera (for scanning product information)

[0442] Server (for data processing and storage)

[0443] software

[0444] OpenCV (for camera operation and image processing)

[0445] TensorFlow / Keras (for emotion recognition models)

[0446] Transformers (generative AI models for sentiment analysis)

[0447] Python standard library (for data processing and communication control)

[0448] Specific examples

[0449] The specific steps for a user to scan a brand-name bag they no longer use at home with the app and find out the purchase price are as follows:

[0450] 1. The user scans the bag's barcode with their smartphone camera.

[0451] 2. The scanned product information is sent to the server.

[0452] 3. The server calculates the purchase price and recommends the optimal buyer and disposal method.

[0453] 4. The emotion engine analyzes the user's emotional state based on their input.

[0454] 5. The interface is optimized according to the user's emotional state, and calculation results and recommendation information are displayed on the user's device.

[0455] Prompt Sentence Examples

[0456] "Please enter a designer bag"

[0457] "Please enter product information: brand name, category, year of purchase, condition"

[0458] "Scan the item with your camera"

[0459] "How are you feeling right now? (free input)"

[0460] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0461] Step 1:

[0462] The user scans a product with the smartphone camera. The user launches the application and uses the camera function to read the product's barcode or QR code. The image data is taken as input, and the barcode information is analyzed using OpenCV. Product information (brand name, category, year of purchase, condition) is obtained as output.

[0463] Step 2:

[0464] The terminal transmits the acquired product information to the server. The terminal structures the product information and transmits it to the server using a secure communication protocol. The terminal receives product information as input and transmits it to the server as output.

[0465] Step 3:

[0466] The server receives the product information and stores it in a database. The server stores the received product information in a database and performs standard database operations to maintain data integrity. The server receives product information as input and stores it in a database as output.

[0467] Step 4:

[0468] The server's purchase price calculation module calculates the purchase price based on the information in the database. The server calculates the purchase price based on the saved product information, referring to past purchase price data for similar products and market trends. It receives product information and market data as input and calculates an approximate purchase price as output.

[0469] Step 5:

[0470] The server's buyback recommendation module searches for the most suitable buyback service. The server searches the database for buyback services that specialize in specific brands or categories and recommends the most suitable buyback service. It receives product information as input and creates a list of recommended buyback services as output.

[0471] Step 6:

[0472] The server's disposal recommendation module provides appropriate disposal methods. For items that are not eligible for purchase, the server suggests appropriate recycling methods or donation information to the user. It receives product information as input and creates a list of disposal methods as output.

[0473] Step 7:

[0474] The server's emotion engine analyzes the user's emotional state. It uses a generative AI model to analyze the emotional state based on data such as the user's free description and operation speed. It receives the user's operation data as input and obtains the user's emotional state as output.

[0475] Step 8:

[0476] The server optimizes the interface according to the emotional state and sends the calculation results and recommendation information to the terminal.The server takes the user's emotional state into consideration and optimizes the interface in simple or detailed form, then sends the calculation results and recommendation information to the terminal.The server receives the emotional state, calculation results, and recommendation information as input, and sends the optimized information to the user's terminal as output.

[0477] Step 9:

[0478] The terminal displays the received information to the user. The terminal displays the calculation results, recommendation information, and optimized interface sent from the server to the user. It receives information from the server as input and visualizes and presents it to the user as output.

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

[0480] 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.

[0481] 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.

[0482] [Second embodiment]

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

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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."

[0495] The system of the present invention supports efficient decluttering by allowing users to easily obtain information on the purchase prices and buyers of unwanted items.

[0496] Overall system configuration

[0497] The system consists of the following main components:

[0498] 1. User Interface (UI): The input form for users to enter product information.

[0499] 2. Server: A central control unit that receives input data from users, stores it in a database, and performs calculations and searches.

[0500] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0501] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0502] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0503] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0504] User inputs and submits product information

[0505] 1. The user enters product information

[0506] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[0507] Example: "Brand bag, purchased in 2018, good condition"

[0508] 2. The device sends product information to the server

[0509] The terminal transmits the collected product information to the server using a secure communication protocol.

[0510] Data storage and purchase price calculation

[0511] 3. The server saves the product information to a database

[0512] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[0513] 4. The purchase price calculation module calculates the purchase price

[0514] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0515] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[0516] Recommendations for buyers and disposal methods

[0517] 5. The buyer recommendation module recommends suitable buyers

[0518] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0519] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0520] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0521] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0522] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0523] Sending and displaying results

[0524] 7. The server sends the calculation results and recommendation information to the device.

[0525] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0526] 8. Display the information received by the device to the user

[0527] The terminal displays the received information, i.e., approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[0528] The system allows users to obtain specific information about unnecessary items in their homes and efficiently progress through the decluttering process.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0532] Step 2:

[0533] The terminal sends the product information entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[0534] Step 3:

[0535] The server stores the received product information in a database, carefully recording attributes such as the product's brand name, category, purchase date, and condition.

[0536] Step 4:

[0537] The server's purchase price calculation module starts processing based on the product information stored in the database. The server calculates an approximate purchase price by referencing past purchase price data and market trend data stored in the database.

[0538] Step 5:

[0539] The server's buyback recommendation module searches the database for buyback services that specialize in specific brands or categories based on product information, and then lists the most suitable buyback services from the search results.

[0540] Step 6:

[0541] If the product has no resale value, the server's disposal recommendation module will suggest disposal options and allow the user to receive information about nearby recycling centers or donation locations.

[0542] Step 7:

[0543] The server sends the purchase price calculation results and recommended buyer information or disposal method to the terminal using a real-time communication protocol.

[0544] Step 8:

[0545] The device displays the received information to the user, who can then decide on their next course of action based on the approximate purchase price, recommended buyers, or disposal methods displayed.

[0546] This process allows users to easily obtain information on the purchase price and buyers of unwanted items from the comfort of their own home, making decluttering a smooth process.

[0547] Example 1

[0548] 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."

[0549] In today's consumer society, many users are looking for efficient ways to dispose of unwanted items. However, it is difficult and time-consuming to find out the purchase price for each item, the best buyer, and the appropriate disposal method for priceless items. Therefore, there is a need for a way for users to easily obtain information on the purchase price and buyers of unwanted items, as well as the appropriate disposal method for priceless items.

[0550] 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.

[0551] In this invention, the server includes means for a user to input product information, means for transmitting the product information to the server, means for storing the product information in a database, means for calculating an approximate purchase price, means for recommending a purchase service specialized in a specific brand or category, means for providing a method for disposing of priceless products, means for transmitting the calculation results and the recommended information to a user terminal, means for displaying to the user the information received by the user terminal, means for transmitting and receiving the product information using a secure communication protocol, means for predicting a purchase price using a machine learning model, and means for acquiring recycling information using an external API. This allows users to easily find out the specific purchase prices and purchase locations for unwanted products, as well as appropriate disposal methods, from the comfort of their own home.

[0552] "User" refers to an individual or organization that uses the system to input product information and obtain purchase prices and purchase destination information.

[0553] "Product Information" means detailed data about a product provided by a user, such as the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0554] The "server" is a computer system that serves as the core of the entire system, receiving and processing product information, calculating purchase prices, recommending buyers, and providing disposal methods.

[0555] A "database" is an information management system that allows a server to store and manage product information, past purchase price data, market trends, purchaser information, and so on.

[0556] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on the product information stored in the database.

[0557] The "Buyback Recommendation Module" is an algorithm or program that recommends buyback services that specialize in specific brands or categories based on product information.

[0558] A "disposal recommendation module" is an algorithm or program that provides information on appropriate disposal, recycling, or donation methods for items that have no price tag.

[0559] A "terminal" is a device such as a smartphone or PC that a user uses to input product information and receive and display calculation results and recommendation information from the server.

[0560] A "secure communication protocol" is a means of securely communicating data between a terminal and a server, and generally includes HTTPS.

[0561] A "machine learning model" is an artificial intelligence algorithm that predicts the purchase price of a product based on past data in a database and market trends.

[0562] "External API" means an external program interface used to obtain recycling information and other related information.

[0563] "Purchase price" is the estimated value of the unwanted item calculated by the purchase price calculation module of the server.

[0564] "Recommended information" refers to information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module of the server.

[0565] This invention relates to a system that allows users to easily obtain information on the purchase price and buyers of unwanted items. The main components of this system are a user interface, a server, a database, a purchase price calculation module, a buyer recommendation module, and a disposal method recommendation module.

[0566] First, the user enters product information using a dedicated application or website. The device used is a smartphone or PC, and the information is entered using this device. The information entered by the user includes the product brand name, category, purchase date, product condition, and, if necessary, an image. For example, the user might enter "brand bag, purchased in 2018, condition: good."

[0567] Next, the device sends the product information to the server. This communication uses a secure communication protocol (such as HTTPS). The communication module in the device calls the API endpoint and sends an HTTP request containing the product information to the server.

[0568] The server stores the received product information in a database. Databases such as MySQL or PostgreSQL are used as the database. A database management program on the server executes an "INSERT INTO" query to store the product information. Based on the stored product information, the server's purchase price calculation module uses a machine learning model (such as TensorFlow) to refer to past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0569] Next, the server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. A Flask application is used to search for buy-back stores, and the obtained buy-back store information is organized and formatted for display. For example, it might say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0570] Furthermore, if the item has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation. To do this, the Node.js program uses an external API to obtain recycling information and compares it with information stored in the database to determine the optimal disposal method. For example, it might say, "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0571] The server then sends the calculated purchase price and recommended buyer information to the device, which then displays this information to the user. Client-side scripts such as JavaScript are used to parse the received data and dynamically update the information in HTML or in the display area within the app. This allows the user to obtain specific information about the purchase price, buyer, and disposal method, and decide on their next course of action.

[0572] As a concrete example, the prompt sentence is shown below.

[0573] Example prompt sentence:

[0574] "Please tell me the details of the program that calculates the purchase price."

[0575] "Please explain the algorithm that recommends the best buyers."

[0576] "What are the steps for recycling products and recommending donation destinations?"

[0577] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[0578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0579] Step 1:

[0580] The user enters product information.

[0581] The user enters the product brand name, category, purchase date, product condition, and, if necessary, an image into an input form on a dedicated application or website. The entered information is internally converted into JSON or form data format. For example, the user enters product information such as "Brand bag, purchased in 2018, condition: good" and clicks the "Submit" button. Input: Detailed product information. Output: Aggregation of input data.

[0582] Step 2:

[0583] The terminal transmits the product information to the server.

[0584] The device sends the stored product information to the server using the HTTPS protocol. The communication module calls the API endpoint and sends a POST request. Input: Product information stored in the device. Output: Request to the server. Specifically, the data is encrypted and divided into packets before being sent.

[0585] Step 3:

[0586] The server stores the product information in a database.

[0587] The server analyzes the product information received from the terminal and saves it in a database. MySQL or PostgreSQL is used for the database. The database management program on the server executes an "INSERT INTO" query to save the product information. Input: Received product information. Output: Results saved in the database. Specifically, data validation is performed to confirm consistency.

[0588] Step 4:

[0589] The purchase price calculation module calculates the purchase price.

[0590] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database. Input data includes past purchase price data for similar products and market trends. A TensorFlow model is used to predict the price, and the results are saved in a temporary data area. Input: Product information in the database. Output: Estimated purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0591] Step 5:

[0592] The buyer recommendation module recommends suitable buyers.

[0593] The server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. The Flask application executes the search query and organizes and formats the buy-back store information obtained. Input: Buy-back store information in the database. Output: Recommended buy-back stores. For example, it would say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best choices."

[0594] Step 6:

[0595] The disposal method recommendation module suggests an appropriate disposal method.

[0596] If the product has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation destinations. The Node.js program retrieves recycling information from an external API and selects the optimal disposal method. Input: Product worthlessness information and recycling API information. Output: Recommended disposal method. A specific example would be, "This product is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0597] Step 7:

[0598] The server sends the calculation results and recommendation information to the terminal.

[0599] The server sends the calculated purchase price, recommended buyer information, and disposal method to the terminal. Data is again sent securely using the HTTPS protocol. Input: Calculation results and recommended information. Output: Data sent to the terminal. The specific operation is to generate an HTTP response.

[0600] Step 8:

[0601] The device displays the received information to the user.

[0602] The device analyzes the received information and displays it to the user. A client-side script such as JavaScript or React parses the data and dynamically displays it on the screen. Input: Data received from the server. Output: Displayed on the user interface. For example, the information displayed might be "Purchase price: 30,000 yen, Recommended purchase locations: Specialized purchase store A, Online purchase service B."

[0603] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[0604] (Application example 1)

[0605] 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."

[0606] Conventional systems for buying unwanted items have the problem that users have to input product information themselves, and then use that information to search for the appropriate buyer, which is time-consuming. Additionally, in certain physical stores, it can be difficult for staff to quickly calculate product purchase prices and provide appropriate recommendations to customers. This makes it difficult to achieve efficient decluttering, and further improvements in convenience are needed.

[0607] 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.

[0608] In this invention, the server includes: means for a user to input product information; means for transmitting the product information to the server; means for the server to store the product information in a database; means for the server to calculate an approximate purchase price based on the product information stored in the database; means for the server to recommend a purchase service specialized in a specific brand or category; means for the server to provide a disposal method for priceless products; means for transmitting the calculation results and recommended information from the server to a user terminal; means for the user terminal to display the received information to the user; means including a terminal for a physical store where customers or staff enter product information; means for inputting a prompt sentence into a generative AI model; and means for obtaining a response from the generative AI model based on the prompt sentence. This automates the entire process from inputting product information to calculating a purchase price and recommending an appropriate buyer, significantly reducing the workload of users and staff and enabling efficient decluttering.

[0609] "Means for users to input product information" refers to interfaces and devices (kiosks, tablets, smart glasses, etc.) that allow customers or staff to input data about products (such as brand name, category, date of purchase, product condition, and images).

[0610] The "means for transmitting the product information to the server" refers to a function and program for transmitting the product information entered by the customer or staff member to the server using a secure communication protocol.

[0611] The "means for the server to store the product information in a database" refers to the processes and techniques for storing the product information received by the server in a database and maintaining consistency and completeness.

[0612] "Means for the server to calculate an approximate purchase price based on the product information stored in the database" refers to the function by which the server applies an algorithm based on the information in the database and market trends to calculate an estimated purchase price for the product.

[0613] "Means for the server to recommend buy-back services specialized in specific brands or categories" refers to a function that searches the database for buy-back services corresponding to specific brands or categories and suggests the most suitable buy-back service to the user.

[0614] "Means for the server to provide methods for disposing of priceless items" refers to a function that provides users with information on appropriate disposal methods, recycling, and donation destinations for items that are not eligible for purchase.

[0615] "Means for transmitting calculation results and recommendation information from the server to the user terminal" refers to a function for transmitting the purchase price and recommendation information calculated by the server to the terminal used by the user via a secure communication protocol.

[0616] "Means for displaying to the user the information received by the user terminal" refers to an interface and program for displaying to the user the purchase price and recommendation information received by the user terminal in an easy-to-see manner.

[0617] "Means including terminals for physical stores where customers or staff can input product information" refers to devices and functions that allow customers to input and submit product information, such as kiosk terminals installed in physical stores or mobile devices used by staff.

[0618] "Means for inputting prompt text into the generative AI model" refers to the interface and functions that allow users and staff to input product information and instructions as text into the generative AI model.

[0619] "Means for obtaining a response from the generative AI model based on the prompt sentence" refers to a function that enables the server to obtain a response generated in response to a prompt input to the generative AI model.

[0620] The system of the present invention is designed to automate and streamline the process of buying unwanted items in brick-and-mortar stores. The basic components are a user interface (UI), a server, a database (DB), a buyback price calculation module, a buyback recommendation module, a disposal method recommendation module, and a generative AI model.

[0621] User inputs and submits product information

[0622] 1. The user enters product information

[0623] Using devices such as kiosks in physical stores or tablets or smart glasses used by staff, users or staff enter product information, including the product brand name, category, purchase date, product condition, and images (if applicable).

[0624] 2. The device sends product information to the server

[0625] The terminal transmits the collected product information to the server using a secure communication protocol (e.g., HTTPS).

[0626] Data storage and purchase price calculation

[0627] 3. The server saves the product information to a database

[0628] Once the server receives the product information, it stores it in a database (e.g., MySQL, PostgreSQL). This process involves standard database operations to ensure data integrity.

[0629] 4. The purchase price calculation module calculates the purchase price

[0630] The purchase price calculation module on the server calculates an approximate purchase price based on product information stored in the database, referring to past purchase price data for similar products and market trends. This module is implemented in a programming language such as Python.

[0631] Recommendations for buyers and disposal methods

[0632] 5. The buyer recommendation module recommends suitable buyers

[0633] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0634] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0635] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0636] Using generative AI models

[0637] 7. Enter a prompt into the generative AI model

[0638] The server generates a prompt sentence based on the input product information and inputs it into a generative AI model (e.g., GPT-3).

[0639] 8. Obtaining responses from generative AI models

[0640] The response generated by the generative AI model is obtained, and the final purchase price and recommendation information are determined based on that content.

[0641] Sending and displaying results

[0642] 9. The server sends the calculation results and recommendation information to the device.

[0643] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0644] 10. Display the information received by the device to the user

[0645] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[0646] Adding specific examples

[0647] For example, if a customer enters product information such as "Brand bag, purchased in 2018, condition: good," the following prompt sentence is input into the generative AI model.

[0648] Example prompt sentence:

[0649] This is a designer bag, purchased in 2018, in good condition. Please let me know the purchase price based on market trends. Also, please recommend any stores or online services that will buy this item.

[0650] Based on this prompt, the generative AI model calculates the purchase price and recommends an appropriate buyer, and the information obtained is sent back to the server and ultimately displayed on the user's or staff's device.

[0651] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0652] Step 1:

[0653] A user or staff member enters product information.

[0654] The terminal provides a function for users or staff to input data about products (brand name, category, purchase date, product condition, image, etc.) through an interface.

[0655] Input: Product information (brand name, category, purchase date, condition, image)

[0656] Output: Input product information data

[0657] Step 2:

[0658] Send product information to the server.

[0659] The terminal transmits the input product information to the server using a secure protocol (for example, HTTPS).

[0660] Input: Entered product information data

[0661] Output: Product information sent to the server

[0662] Step 3:

[0663] Store product information in a database.

[0664] The server stores the received product information in a database (MySQL or PostgreSQL), performing appropriate database operations to maintain data integrity and consistency.

[0665] Input: Product information sent to the server

[0666] Output: Product information stored in the database

[0667] Step 4:

[0668] Calculate the purchase price.

[0669] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database, market trends, and past purchase price data.

[0670] Input: Product information stored in the database, past purchase price data, market trend data

[0671] Output: Approximate purchase price

[0672] Step 5:

[0673] Recommend suitable buyers.

[0674] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0675] Input: Product information, purchase price, and purchase service data stored in the database

[0676] Output: Recommended buyer information

[0677] Step 6:

[0678] Propose appropriate disposal methods.

[0679] The server's disposal recommendation module provides users with information on appropriate disposal, recycling, and donation options for items that have no value or cannot be sold.

[0680] Input: Product information stored in the database

[0681] Output: Recommended disposal, recycling, or donation information

[0682] Step 7:

[0683] Input a prompt sentence into the generative AI model.

[0684] The server generates a prompt sentence based on the input product information and inputs it into the generative AI model.

[0685] Input: Product information, prompt text

[0686] Output: The prompt sent to the generative AI model

[0687] Step 8:

[0688] Obtain a response from the generative AI model.

[0689] The server obtains the response generated by the generative AI model and determines the final purchase price and recommendation information based on the content.

[0690] Input: Response from a generative AI model

[0691] Output: Final purchase price, recommendation information

[0692] Step 9:

[0693] The calculation results and recommendation information are transmitted to the terminal.

[0694] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0695] Input: Final purchase price, recommendation information

[0696] Output: Buy price and recommendation information sent to the terminal

[0697] Step 10:

[0698] Display the received information to the user.

[0699] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[0700] Input: Purchase price and recommendation information sent to the terminal

[0701] Output: Buy price and recommendation displayed to user

[0702] 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.

[0703] The system of the present invention not only allows users to easily obtain information on the purchase price and buyers of unwanted items, but also provides more appropriate information and optimizes the interface by taking into account the user's emotional state.

[0704] Overall system configuration

[0705] The system consists of the following main components:

[0706] 1. User Interface (UI): The input form for users to enter product information.

[0707] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[0708] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0709] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0710] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0711] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0712] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[0713] User inputs and submits product information

[0714] 1. The user enters product information

[0715] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[0716] Example: "Brand bag, purchased in 2018, good condition"

[0717] 2. The device sends product information to the server

[0718] The terminal sends the product information entered by the user to the server using a secure communication protocol.

[0719] Data storage and purchase price calculation

[0720] 3. The server saves the product information to a database

[0721] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[0722] 4. The purchase price calculation module calculates the purchase price

[0723] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0724] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[0725] Recommendations for buyers and disposal methods

[0726] 5. The buyer recommendation module recommends suitable buyers

[0727] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0728] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0729] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0730] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0731] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0732] Emotion Recognition and Interface Optimization

[0733] 7. Emotion engine recognizes the user's emotional state

[0734] The emotion engine analyzes the user's emotional state based on their operation and input, including the speed of their operation, frequency of their input, and text mining of their input.

[0735] Example: If a user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly.

[0736] 8. Emotional engine optimizes the interface

[0737] Based on the user's emotional state, the emotion engine changes the interface's appearance and operation, for example, providing more detailed instructions and reducing the number of steps required if the user is feeling anxious.

[0738] Sending and displaying results

[0739] 9. The server sends the calculation results and recommendation information to the device.

[0740] The server transmits the calculated purchase price, recommended purchaser information, or disposal method to the terminal.

[0741] 10. Display the information received by the device to the user

[0742] The terminal displays the received information, such as the approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[0743] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support tailored to their emotional state.

[0744] The processing flow will be explained below.

[0745] Step 1:

[0746] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0747] Step 2:

[0748] The terminal transmits the product information entered by the user to the server using a secure communication protocol.

[0749] Step 3:

[0750] The server receives the product information and stores it in a database, properly recording information such as the product's brand name, category, purchase date, and condition.

[0751] Step 4:

[0752] The server's purchase price calculation module calculates the purchase price based on the product information stored in the database, referring to past purchase price data and market trends to calculate an approximate purchase price.

[0753] Step 5:

[0754] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and selects the most suitable buyer.

[0755] Step 6:

[0756] If the product has no value or no price, the server's disposal recommendation module searches for information on the appropriate disposal method, recycling, or donation destination.

[0757] Step 7:

[0758] The emotion engine analyzes the user's input and operational status to recognize their emotional state. For example, if the user is typing quickly, it will determine that they are feeling impatient.

[0759] Step 8:

[0760] An emotion engine dynamically changes the interface based on the user's perceived emotional state, for example simplifying instructions or using more friendly language for an impatient user.

[0761] Step 9:

[0762] The server then sends the calculated purchase price and recommended buyer information or disposal method to the terminal. This transmission is also carried out using a secure communication protocol.

[0763] Step 10:

[0764] The device displays the received information to the user, including an approximate purchase price, recommended buyers, or disposal methods, allowing the user to decide on the next course of action.

[0765] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and with the support of an emotion engine, they can receive the most appropriate information tailored to their situation.

[0766] Example 2

[0767] 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."

[0768] In conventional buying and selling systems, users can input product information and obtain information on buying prices and buyers, but the system does not take into account the user's emotional state when providing information or optimizing the interface, which can be stressful for users. Furthermore, the system also faces issues such as inefficiency and inaccuracy in calculating product buying prices and recommending buyers.

[0769] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0770] In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing the interface based on the emotional state, means for calculating an approximate purchase price based on product information, means for recommending a purchase service specialized for a specific brand or category, means for providing a method for disposing of priceless products, and means for transmitting the calculation results and recommendation information from the server to the user terminal. This allows the user to reduce stress while efficiently and accurately calculating the purchase price and receiving recommendations for buyers.

[0771] "User" refers to an individual or corporation that uses the system to request information on purchase prices and disposal methods for unwanted items.

[0772] "Product information" refers to detailed information about the unwanted product entered by the user, such as the brand name, category, purchase date, product condition, and image.

[0773] The "server" is a central control device that receives product information sent by users, processes it, and sends the results to the user terminal.

[0774] A "database" is an information management system that stores and manages data such as product information, past purchase price data, purchaser information, and market trends.

[0775] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on product information stored in a database, by referring to past purchase price data and market trends.

[0776] The "buyer recommendation module" is an algorithm or program that searches a database for the most suitable buyer based on product information and recommends it to the user.

[0777] A "disposal recommendation module" is an algorithm or program that provides users with information on appropriate disposal, recycling, or donation options for items that have no value.

[0778] An "emotion engine" is a program that analyzes the user's operation speed and input content to recognize their emotional state and reflect this in the system's operation.

[0779] A "user terminal" is a device (such as a smartphone or PC) that a user uses to input product information and receive and display the results from the server.

[0780] A "secure communication protocol" is a communication method that ensures security when sending and receiving data, such as HTTPS.

[0781] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides appropriate information and optimizes the interface by taking into account the user's emotional state.

[0782] Overall system configuration

[0783] The system consists of the following main components:

[0784] 1. User Interface (UI): The input form for users to enter product information.

[0785] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[0786] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0787] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0788] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0789] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0790] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[0791] 8. User terminal: A device (e.g., a smartphone or PC) on which a user enters product information and receives and displays the results from the server.

[0792] User inputs and submits product information

[0793] The user uses a device to input product information into an input form on a dedicated application or website. This information includes the product brand name, category, purchase date, product condition, and an image (if necessary). For example, the user can input information such as "Brand bag, purchased in 2018, condition: good." The input product information is sent from the device to the server using a secure communication protocol (e.g., HTTPS).

[0794] Data storage and purchase price calculation

[0795] The server stores the received product information in a database. This storage process involves standard database operations (e.g., SQL queries) to maintain data integrity. Based on the stored product information, a purchase price calculation module calculates the purchase price. This module references past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, the calculation might be "brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[0796] Recommendations for buyers and disposal methods

[0797] The buy-back recommendation module can search a database for the most suitable buy-back store based on product information and recommend it to the user. For example, it might recommend, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best." Furthermore, for items that have no value, the disposal recommendation module will suggest the appropriate disposal method to the user. For example, it might suggest, "This item is not eligible for purchase. We recommend that you take it to a recycling center or donate it to 'NPO C.'"

[0798] Emotion Recognition and Interface Optimization

[0799] The emotion engine analyzes the user's speed and input to recognize their emotional state. This analysis uses text mining and natural language processing (NLP). For example, if the user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly. Based on the emotional state, the emotion engine can change the UI layout to optimize the user experience.

[0800] Sending and displaying results

[0801] The server sends the calculated purchase price, recommended buyer information, and disposal method to the device, which then displays the received information to the user, allowing the user to decide on their next action based on the information obtained.

[0802] Example: A user enters "brand bag, purchased in 2018, condition: good," and the device sends this to the server. The server uses a purchase price calculation module to calculate an approximate price, and displays "The purchase price for this bag is 30,000 yen" on the device. The device also displays recommendation information such as "The best places for this brand bag are 'Specialized Purchase Store A' and 'Online Purchase Service B'."

[0803] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support based on their emotional state.

[0804] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0805] Step 1:

[0806] User enters product information

[0807] Users use their devices to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and an image (if necessary).

[0808] Input: Manual input by the user

[0809] Output: Product information temporarily saved on the device

[0810] Specific action: For example, the user enters "brand bag, purchased in 2018, condition: good."

[0811] Step 2:

[0812] The device sends product information to the server

[0813] The terminal sends the entered product information to the server using a secure communication protocol (e.g., HTTPS).

[0814] Input: User-entered data

[0815] Output: Product information that arrives at the server

[0816] Specific operation: The terminal encrypts the product information using SSL / TLS and sends it to the server.

[0817] Step 3:

[0818] The server saves the product information to a database

[0819] The server stores the received product information in a database, which involves standard database operations (e.g., SQL queries) to ensure data integrity.

[0820] Input: Product information that arrives at the server

[0821] Output: Product information stored in a database

[0822] Specific behavior: Executes an SQL query to insert product information into the database. "INSERT INTO ProductInfo (BrandName, Category, PurchaseDate, Condition, Image) VALUES ('Brand Bag', 'Bag', '2018-01-01', 'Good', 'image_path');"

[0823] Step 4:

[0824] The purchase price calculation module calculates the purchase price

[0825] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0826] Input: Product information stored in the database

[0827] Output: Calculated approximate purchase price

[0828] How it works: The buyback price calculation algorithm compares past buyback price data and market trend data, and then uses statistical analysis and machine learning models to predict prices. "It uses product information as model input and outputs an estimated price."

[0829] Step 5:

[0830] The buyer recommendation module recommends suitable buyers

[0831] The server's buyer recommendation module searches a database for buying services that specialize in the product brand or category, and recommends the most suitable buyer to the user.

[0832] Input: Product information stored in the database

[0833] Output: Recommended buyer list

[0834] Specific behavior: Executes an SQL query to search for buyback services for the relevant brand and category, and generates a list of the best buyback services. "SELECT FROM BuybackServices WHERE Category = 'Bags' AND Brand = 'Brand Bags';"

[0835] Step 6:

[0836] The disposal method recommendation module suggests appropriate disposal methods.

[0837] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0838] Input: Product information stored in the database

[0839] Output: List of recommended disposal methods

[0840] Specific behavior: Runs an SQL query to find and generate a list of disposal methods for the given category: SELECT FROM DisposalMethods WHERE Category = 'Bag';

[0841] Step 7:

[0842] Emotion engine recognizes the user's emotional state

[0843] The emotion engine analyzes the user's emotional state based on their operation and input, including operation speed, input frequency, and text mining of input content.

[0844] Input: User operation data and input data

[0845] Output: Perceived emotional state

[0846] Specific operation: Text mining is performed on the user's operation speed and input content, and the input is input into the emotion analysis model to output the emotion. "emotion_result = emotion_analysis_module.analyze(user_input)"

[0847] Step 8:

[0848] Emotional engine optimizes the interface

[0849] The emotion engine changes the look and feel of the interface based on the user's emotional state, for example by providing more detailed instructions or reducing the number of steps required.

[0850] Input: Perceived emotional state

[0851] Output: Optimized interface

[0852] What it does: The emotion engine dynamically changes UI components to display the appropriate message and layout for the user. "Sorry for the wait. I'll make this as easy as possible."

[0853] Step 9:

[0854] The server sends the calculation results and recommendation information to the device.

[0855] The server sends the calculated purchase price, recommended buyer information, and disposal method to the user's terminal.

[0856] Input: Purchase price, purchase destination list, disposal method list

[0857] Output: Calculation results and recommendations sent to the device

[0858] Specific operation: The server structures the result data through the API and sends it to the terminal. "The JSON format data is sent to the terminal via the API."

[0859] Step 10:

[0860] Displaying information received by the device to the user

[0861] The device displays the received information to the user in a way that is easy for the user to understand.

[0862] Input: Calculation results and recommendations received from the server

[0863] Output: Information displayed to the user

[0864] Specific operation: The device displays the purchase price and purchase destination information in a table format on the UI. "Purchase price: 30,000 yen Recommended purchase destinations: Specialized purchase store A, online purchase service B Disposal method: Recycling center D, donation to NPO C"

[0865] (Application example 2)

[0866] 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."

[0867] In conventional buy-back service systems, users often find it difficult to obtain information on the buy-back price and buyers of unwanted items, and the information provided does not reflect the user's emotional state, leaving many users dissatisfied. Furthermore, the lack of interface optimization that takes into account the user's emotional state results in a poor user experience.

[0868] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and optimizing the interface, means for the user to input product information, means for transmitting the product information to the server, means for the server to store the product information in a database, means for the server to calculate an approximate purchase price based on the product information stored in the database, means for the server to recommend a purchase service specialized in a specific brand or category, means for the server to provide a method for disposing of priceless products, means for transmitting the calculation results and recommended information from the server to a user terminal, and means for the user terminal to display the received information to the user. This enables users to easily obtain purchase prices and purchase destination information for unwanted products and to receive optimal support tailored to their emotional state.

[0869] "User interface" refers to the screen layout and operating means that allow users to input product information, etc.

[0870] "Server" means a central control device that receives, stores, processes data and provides information to users.

[0871] A "database" is an information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0872] A "purchase price calculation module" is software that includes an algorithm that calculates an approximate purchase price based on stored product information.

[0873] A "buyer recommendation module" is software that includes an algorithm that recommends appropriate buyers based on product information.

[0874] The "disposal recommendation module" is software that includes an algorithm that suggests appropriate disposal, recycling, or donation options for items that have no price tag.

[0875] An "emotion engine" is software that recognizes the user's emotional state and reflects it in the operation of the entire system.

[0876] "Emotional state" refers to the user's current psychological state, analyzed based on factors such as the user's operation speed, input frequency, and text mining of input content.

[0877] "Interface optimization" refers to changing the appearance and operation of an interface depending on the user's emotional state.

[0878] A "user terminal" is a device operated by a user, such as a smartphone.

[0879] "Product information" refers to data entered by the user, such as the product brand name, category, purchase date, product condition, and images.

[0880] The "calculation result" refers to the approximate purchase price calculated by the purchase price calculation module.

[0881] "Recommendation information" is information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module.

[0882] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides optimal support according to the user's emotional state. The overall configuration of this system is as follows.

[0883] Overall system configuration

[0884] 1. User Interface (UI)

[0885] Users use a smartphone application to input product information, which has a function to scan barcodes and QR codes and automatically input the scanned product information.

[0886] 2. Emotion Engine

[0887] The system analyzes the user's emotional state based on their input speed and operation method. It uses a generative AI model for emotion analysis and determines the user's emotional state through text mining of the input content.

[0888] 3. Server

[0889] Product information storage: Product information sent by users is saved on the server and stored in a database. Product information includes brand name, category, purchase date, product condition, images, etc.

[0890] Calculation of purchase price: The purchase price calculation module on the server calculates the purchase price based on the product information stored in the database. The calculation is performed based on past purchase price data for similar products and market trends.

[0891] Buyer recommendation: The buy-back recommendation module on the server searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back service to the user.

[0892] Providing disposal methods: For items that are not eligible for purchase, a disposal recommendation module on the server will suggest recycling methods or donation destinations to users.

[0893] 4. User Device

[0894] The system receives calculation results and recommendation information sent from the server and displays them to the user. It also optimizes the interface according to the user's emotional state. For example, if the user is feeling anxious, it will display more detailed explanations to reassure them.

[0895] Hardware and software used

[0896] Hardware

[0897] Smartphone camera (for scanning product information)

[0898] Server (for data processing and storage)

[0899] software

[0900] OpenCV (for camera operation and image processing)

[0901] TensorFlow / Keras (for emotion recognition models)

[0902] Transformers (generative AI models for sentiment analysis)

[0903] Python standard library (for data processing and communication control)

[0904] Specific examples

[0905] The specific steps for a user to scan a brand-name bag they no longer use at home with the app and find out the purchase price are as follows:

[0906] 1. The user scans the bag's barcode with their smartphone camera.

[0907] 2. The scanned product information is sent to the server.

[0908] 3. The server calculates the purchase price and recommends the optimal buyer and disposal method.

[0909] 4. The emotion engine analyzes the user's emotional state based on their input.

[0910] 5. The interface is optimized according to the user's emotional state, and calculation results and recommendation information are displayed on the user's device.

[0911] Prompt Sentence Examples

[0912] "Please enter designer bag"

[0913] "Please enter product information: brand name, category, year of purchase, condition"

[0914] "Scan the product with your camera"

[0915] "How are you feeling right now? (free input)"

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] The user scans a product with the smartphone camera. The user launches the application and uses the camera function to read the product's barcode or QR code. The image data is taken as input, and the barcode information is analyzed using OpenCV. Product information (brand name, category, year of purchase, condition) is obtained as output.

[0919] Step 2:

[0920] The terminal transmits the acquired product information to the server. The terminal structures the product information and transmits it to the server using a secure communication protocol. The terminal receives product information as input and transmits it to the server as output.

[0921] Step 3:

[0922] The server receives the product information and stores it in a database. The server stores the received product information in a database and performs standard database operations to maintain data integrity. The server receives product information as input and stores it in a database as output.

[0923] Step 4:

[0924] The server's purchase price calculation module calculates the purchase price based on the information in the database. The server calculates the purchase price based on the stored product information, referring to past purchase price data for similar products and market trends. It receives product information and market data as input and calculates an approximate purchase price as output.

[0925] Step 5:

[0926] The server's buyback recommendation module searches for the most suitable buyback service. The server searches the database for buyback services that specialize in specific brands or categories and recommends the most suitable buyback service. It receives product information as input and creates a list of recommended buyback services as output.

[0927] Step 6:

[0928] The server's disposal recommendation module provides appropriate disposal methods. For items that are not eligible for purchase, the server suggests appropriate recycling methods or donation information to the user. It receives product information as input and creates a list of disposal methods as output.

[0929] Step 7:

[0930] The server's emotion engine analyzes the user's emotional state. It uses a generative AI model to analyze the emotional state based on data such as the user's free description and operation speed. It receives the user's operation data as input and obtains the user's emotional state as output.

[0931] Step 8:

[0932] The server optimizes the interface according to the emotional state and sends the calculation results and recommendation information to the terminal.The server takes the user's emotional state into consideration and optimizes the interface in simple or detailed form, then sends the calculation results and recommendation information to the terminal.The server receives the emotional state, calculation results, and recommendation information as input, and sends the optimized information to the user's terminal as output.

[0933] Step 9:

[0934] The terminal displays the received information to the user. The terminal displays the calculation results, recommendation information, and optimized interface sent from the server to the user. It receives information from the server as input and visualizes and presents it to the user as output.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 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.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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."

[0951] The system of the present invention supports efficient decluttering by allowing users to easily obtain information on the purchase prices and buyers of unwanted items.

[0952] Overall system configuration

[0953] The system consists of the following main components:

[0954] 1. User Interface (UI): The input form for users to enter product information.

[0955] 2. Server: A central control unit that receives input data from users, stores it in a database, and performs calculations and searches.

[0956] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[0957] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[0958] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[0959] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[0960] User inputs and submits product information

[0961] 1. The user enters product information

[0962] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[0963] Example: "Brand bag, purchased in 2018, good condition"

[0964] 2. The device sends product information to the server

[0965] The terminal transmits the collected product information to the server using a secure communication protocol.

[0966] Data storage and purchase price calculation

[0967] 3. The server saves the product information to a database

[0968] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[0969] 4. The purchase price calculation module calculates the purchase price

[0970] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[0971] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[0972] Recommendations for buyers and disposal methods

[0973] 5. The buyer recommendation module recommends suitable buyers

[0974] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[0975] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[0976] 6. The disposal method recommendation module suggests an appropriate disposal method.

[0977] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[0978] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[0979] Sending and displaying results

[0980] 7. The server sends the calculation results and recommendation information to the device.

[0981] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[0982] 8. Display the information received by the device to the user

[0983] The terminal displays the received information, i.e., approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[0984] The system allows users to obtain specific information about unnecessary items in their homes and efficiently progress through the decluttering process.

[0985] The processing flow will be explained below.

[0986] Step 1:

[0987] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[0988] Step 2:

[0989] The terminal sends the product information entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[0990] Step 3:

[0991] The server stores the received product information in a database, carefully recording attributes such as the product's brand name, category, purchase date, and condition.

[0992] Step 4:

[0993] The server's purchase price calculation module starts processing based on the product information stored in the database. The server calculates an approximate purchase price by referencing past purchase price data and market trend data stored in the database.

[0994] Step 5:

[0995] The server's buyback recommendation module searches the database for buyback services that specialize in specific brands or categories based on product information, and then lists the most suitable buyback services from the search results.

[0996] Step 6:

[0997] If the product has no resale value, the server's disposal recommendation module will suggest disposal options and allow the user to receive information about nearby recycling centers or donation locations.

[0998] Step 7:

[0999] The server sends the purchase price calculation results and recommended buyer information or disposal method to the terminal using a real-time communication protocol.

[1000] Step 8:

[1001] The device displays the received information to the user, who can then decide on their next course of action based on the approximate purchase price, recommended buyers, or disposal methods displayed.

[1002] This process allows users to easily obtain information on the purchase price and buyers of unwanted items from the comfort of their own home, making decluttering a smooth process.

[1003] Example 1

[1004] 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."

[1005] In today's consumer society, many users are looking for efficient ways to dispose of unwanted items. However, it is difficult and time-consuming to find out the purchase price for each item, the best buyer, and the appropriate disposal method for priceless items. Therefore, there is a need for a way for users to easily obtain information on the purchase price and buyers of unwanted items, as well as the appropriate disposal method for priceless items.

[1006] 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.

[1007] In this invention, the server includes means for a user to input product information, means for transmitting the product information to the server, means for storing the product information in a database, means for calculating an approximate purchase price, means for recommending a purchase service specialized in a specific brand or category, means for providing a method for disposing of priceless products, means for transmitting the calculation results and the recommended information to a user terminal, means for displaying information received by the user terminal to the user, means for transmitting and receiving the product information using a secure communication protocol, means for predicting a purchase price using a machine learning model, and means for acquiring recycling information using an external API. This allows users to easily find out the specific purchase prices and purchase locations for unwanted products, as well as appropriate disposal methods, from the comfort of their own home.

[1008] "User" refers to an individual or organization that uses the system to input product information and obtain purchase prices and purchase destination information.

[1009] "Product Information" means detailed data about a product provided by a user, such as the product brand name, category, purchase date, product condition, and, if necessary, product images.

[1010] The "server" is a computer system that serves as the core of the entire system, receiving and processing product information, calculating purchase prices, recommending buyers, and providing disposal methods.

[1011] A "database" is an information management system that allows a server to store and manage product information, past purchase price data, market trends, purchaser information, and so on.

[1012] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on the product information stored in the database.

[1013] The "Buyback Recommendation Module" is an algorithm or program that recommends buyback services that specialize in specific brands or categories based on product information.

[1014] A "disposal recommendation module" is an algorithm or program that provides information on appropriate disposal methods, recycling, and donation destinations for items that have no price tag.

[1015] A "terminal" is a device such as a smartphone or PC that a user uses to input product information and receive and display calculation results and recommendation information from the server.

[1016] A "secure communication protocol" is a means of securely communicating data between a terminal and a server, and generally includes HTTPS.

[1017] A "machine learning model" is an artificial intelligence algorithm that predicts the purchase price of a product based on past data in a database and market trends.

[1018] "External API" means an external program interface used to obtain recycling information and other related information.

[1019] "Purchase price" is the estimated value of the unwanted item calculated by the purchase price calculation module of the server.

[1020] "Recommended information" refers to information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module of the server.

[1021] This invention relates to a system that allows users to easily obtain information on the purchase price and buyers of unwanted items. The main components of this system are a user interface, a server, a database, a purchase price calculation module, a buyer recommendation module, and a disposal method recommendation module.

[1022] First, the user enters product information using a dedicated application or website. The device used is a smartphone or PC, and the information is entered using this device. The information entered by the user includes the product brand name, category, purchase date, product condition, and, if necessary, an image. For example, the user might enter "brand bag, purchased in 2018, condition: good."

[1023] Next, the device sends the product information to the server. This communication uses a secure communication protocol (such as HTTPS). The communication module in the device calls the API endpoint and sends an HTTP request containing the product information to the server.

[1024] The server stores the received product information in a database. Databases such as MySQL or PostgreSQL are used as the database. A database management program on the server executes an "INSERT INTO" query to store the product information. Based on the stored product information, the server's purchase price calculation module uses a machine learning model (such as TensorFlow) to refer to past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1025] Next, the server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. A Flask application is used to search for buy-back stores, and the obtained buy-back store information is organized and formatted for display. For example, it might say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[1026] Furthermore, if the item has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation. To do this, the Node.js program uses an external API to obtain recycling information and compares it with information stored in the database to determine the optimal disposal method. For example, it might say, "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1027] The server then sends the calculated purchase price and recommended buyer information to the device, which then displays this information to the user. Client-side scripts such as JavaScript are used to parse the received data and dynamically update the information in HTML or in the display area within the app. This allows the user to obtain specific information about the purchase price, buyer, and disposal method, and decide on their next course of action.

[1028] As a concrete example, the prompt sentence is shown below.

[1029] Example prompt sentence:

[1030] "Please tell me the details of the program that calculates the purchase price."

[1031] "Please explain the algorithm that recommends the best buyers."

[1032] "What are the steps for recycling products and recommending donation destinations?"

[1033] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[1034] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1035] Step 1:

[1036] The user enters product information.

[1037] The user enters the product brand name, category, purchase date, product condition, and, if necessary, an image into an input form on a dedicated application or website. The entered information is internally converted into JSON or form data format. For example, the user enters product information such as "Brand bag, purchased in 2018, condition: good" and clicks the "Submit" button. Input: Detailed product information. Output: Aggregation of input data.

[1038] Step 2:

[1039] The terminal transmits the product information to the server.

[1040] The device sends the stored product information to the server using the HTTPS protocol. The communication module calls the API endpoint and sends a POST request. Input: Product information stored in the device. Output: Request to the server. Specifically, the data is encrypted and divided into packets before being sent.

[1041] Step 3:

[1042] The server stores the product information in a database.

[1043] The server analyzes the product information received from the terminal and saves it in a database. MySQL or PostgreSQL is used for the database. The database management program on the server executes an "INSERT INTO" query to save the product information. Input: Received product information. Output: Results saved in the database. Specifically, data validation is performed to confirm consistency.

[1044] Step 4:

[1045] The purchase price calculation module calculates the purchase price.

[1046] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database. Input data includes past purchase price data for similar products and market trends. A TensorFlow model is used to predict the price, and the results are saved in a temporary data area. Input: Product information in the database. Output: Estimated purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1047] Step 5:

[1048] The buyer recommendation module recommends suitable buyers.

[1049] The server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. The Flask application executes the search query and organizes and formats the buy-back store information obtained. Input: Buy-back store information in the database. Output: Recommended buy-back stores. For example, it would say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best choices."

[1050] Step 6:

[1051] The disposal method recommendation module suggests an appropriate disposal method.

[1052] If the product has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation destinations. The Node.js program retrieves recycling information from an external API and selects the optimal disposal method. Input: Product worthlessness information and recycling API information. Output: Recommended disposal method. A specific example would be, "This product is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1053] Step 7:

[1054] The server sends the calculation results and recommendation information to the terminal.

[1055] The server sends the calculated purchase price, recommended buyer information, and disposal method to the terminal. Data is again sent securely using the HTTPS protocol. Input: Calculation results and recommended information. Output: Data sent to the terminal. The specific operation is to generate an HTTP response.

[1056] Step 8:

[1057] The device displays the received information to the user.

[1058] The device analyzes the received information and displays it to the user. A client-side script such as JavaScript or React parses the data and dynamically displays it on the screen. Input: Data received from the server. Output: Displayed on the user interface. For example, the information displayed might be "Purchase price: 30,000 yen, Recommended purchase locations: Specialized purchase store A, Online purchase service B."

[1059] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[1060] (Application example 1)

[1061] 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."

[1062] Conventional systems for buying unwanted items have the problem that users have to input product information themselves, and then use that information to search for the appropriate buyer, which is time-consuming. Additionally, in certain physical stores, it can be difficult for staff to quickly calculate product purchase prices and provide appropriate recommendations to customers. This makes it difficult to achieve efficient decluttering, and further improvements in convenience are needed.

[1063] 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.

[1064] In this invention, the server includes: means for a user to input product information; means for transmitting the product information to the server; means for the server to store the product information in a database; means for the server to calculate an approximate purchase price based on the product information stored in the database; means for the server to recommend a purchase service specialized in a specific brand or category; means for the server to provide a disposal method for priceless products; means for transmitting the calculation results and recommended information from the server to a user terminal; means for the user terminal to display the received information to the user; means including a terminal for a physical store where customers or staff enter product information; means for inputting a prompt sentence into a generative AI model; and means for obtaining a response from the generative AI model based on the prompt sentence. This automates the entire process from inputting product information to calculating a purchase price and recommending an appropriate buyer, significantly reducing the workload of users and staff and enabling efficient decluttering.

[1065] "Means for users to input product information" refers to interfaces and devices (kiosks, tablets, smart glasses, etc.) that allow customers or staff to input data about products (such as brand name, category, date of purchase, product condition, and images).

[1066] The "means for transmitting the product information to the server" refers to a function and program for transmitting the product information entered by the customer or staff member to the server using a secure communication protocol.

[1067] The "means for the server to store the product information in a database" refers to the processes and techniques for storing the product information received by the server in a database and maintaining consistency and completeness.

[1068] "Means for the server to calculate an approximate purchase price based on the product information stored in the database" refers to the function by which the server applies an algorithm based on the information in the database and market trends to calculate an estimated purchase price for the product.

[1069] "Means for the server to recommend buy-back services specialized in specific brands or categories" refers to a function that searches the database for buy-back services corresponding to specific brands or categories and suggests the most suitable buy-back service to the user.

[1070] "Means for the server to provide methods for disposing of priceless items" refers to a function that provides users with information on appropriate disposal methods, recycling, and donation destinations for items that are not eligible for purchase.

[1071] "Means for transmitting calculation results and recommendation information from the server to the user terminal" refers to a function for transmitting the purchase price and recommendation information calculated by the server to the terminal used by the user via a secure communication protocol.

[1072] "Means for displaying to the user the information received by the user terminal" refers to an interface and program for displaying to the user the purchase price and recommendation information received by the user terminal in an easy-to-see manner.

[1073] "Means including terminals for physical stores where customers or staff can input product information" refers to devices and functions that allow customers to input and submit product information, such as kiosk terminals installed in physical stores or mobile devices used by staff.

[1074] "Means for inputting prompt text into the generative AI model" refers to the interface and functions that allow users and staff to input product information and instructions in text form into the generative AI model.

[1075] "Means for obtaining a response from the generative AI model based on the prompt sentence" refers to a function that enables the server to obtain a response generated in response to a prompt input to the generative AI model.

[1076] The system of the present invention is designed to automate and streamline the process of buying unwanted items in brick-and-mortar stores. The basic components are a user interface (UI), a server, a database (DB), a buyback price calculation module, a buyback store recommendation module, a disposal method recommendation module, and a generative AI model.

[1077] User inputs and submits product information

[1078] 1. The user enters product information

[1079] Using devices such as kiosks in physical stores or tablets or smart glasses used by staff, users or staff enter product information, including the product brand name, category, purchase date, product condition, and images (if applicable).

[1080] 2. The device sends product information to the server

[1081] The terminal transmits the collected product information to the server using a secure communication protocol (e.g., HTTPS).

[1082] Data storage and purchase price calculation

[1083] 3. The server saves the product information to a database

[1084] Once the server receives the product information, it stores it in a database (e.g., MySQL, PostgreSQL). This process involves standard database operations to ensure data integrity.

[1085] 4. The purchase price calculation module calculates the purchase price

[1086] The purchase price calculation module on the server calculates an approximate purchase price based on product information stored in the database, referring to past purchase price data for similar products and market trends. This module is implemented in a programming language such as Python.

[1087] Recommendations for buyers and disposal methods

[1088] 5. The buyer recommendation module recommends suitable buyers

[1089] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1090] 6. The disposal method recommendation module suggests an appropriate disposal method.

[1091] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1092] Using generative AI models

[1093] 7. Enter a prompt into the generative AI model

[1094] The server generates a prompt sentence based on the input product information and inputs it into a generative AI model (e.g., GPT-3).

[1095] 8. Obtaining responses from generative AI models

[1096] The response generated by the generative AI model is obtained, and the final purchase price and recommendation information are determined based on that content.

[1097] Sending and displaying results

[1098] 9. The server sends the calculation results and recommendation information to the device.

[1099] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[1100] 10. Display the information received by the device to the user

[1101] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[1102] Adding specific examples

[1103] For example, if a customer enters product information such as "Brand bag, purchased in 2018, condition: good," the following prompt sentence is input into the generative AI model.

[1104] Example prompt sentence:

[1105] This is a designer bag, purchased in 2018, in good condition. Please let me know the purchase price based on market trends. Also, please recommend any stores or online services that will buy this item.

[1106] Based on this prompt, the generative AI model calculates the purchase price and recommends an appropriate buyer, and the information obtained is sent back to the server and ultimately displayed on the user's or staff's device.

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] A user or staff member enters product information.

[1110] The terminal provides a function for users or staff to input data about products (brand name, category, purchase date, product condition, image, etc.) through an interface.

[1111] Input: Product information (brand name, category, purchase date, condition, image)

[1112] Output: Input product information data

[1113] Step 2:

[1114] Product information is sent to the server.

[1115] The terminal transmits the input product information to the server using a secure protocol (for example, HTTPS).

[1116] Input: Entered product information data

[1117] Output: Product information sent to the server

[1118] Step 3:

[1119] Store product information in a database.

[1120] The server stores the received product information in a database (MySQL or PostgreSQL), performing appropriate database operations to maintain data integrity and consistency.

[1121] Input: Product information sent to the server

[1122] Output: Product information stored in the database

[1123] Step 4:

[1124] Calculate the purchase price.

[1125] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database, market trends, and past purchase price data.

[1126] Input: Product information stored in the database, past purchase price data, market trend data

[1127] Output: Approximate purchase price

[1128] Step 5:

[1129] Recommend suitable buyers.

[1130] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1131] Input: Product information, purchase price, and purchase service data stored in the database

[1132] Output: Recommended buyer information

[1133] Step 6:

[1134] Propose appropriate disposal methods.

[1135] The server's disposal recommendation module provides users with information on appropriate disposal, recycling, and donation options for items that have no value or are otherwise invaluable.

[1136] Input: Product information stored in the database

[1137] Output: Recommended disposal, recycling, or donation information

[1138] Step 7:

[1139] Input a prompt sentence into the generative AI model.

[1140] The server generates a prompt sentence based on the input product information and inputs it into the generative AI model.

[1141] Input: Product information, prompt text

[1142] Output: The prompt sent to the generative AI model

[1143] Step 8:

[1144] Obtain a response from the generative AI model.

[1145] The server obtains the response generated by the generative AI model and determines the final purchase price and recommendation information based on the content.

[1146] Input: Response from a generative AI model

[1147] Output: Final purchase price, recommendation information

[1148] Step 9:

[1149] The calculation results and recommendation information are transmitted to the terminal.

[1150] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[1151] Input: Final purchase price, recommendation information

[1152] Output: Buy price and recommendation information sent to the terminal

[1153] Step 10:

[1154] Display the received information to the user.

[1155] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[1156] Input: Purchase price and recommendation information sent to the terminal

[1157] Output: Buy price and recommendation displayed to user

[1158] 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.

[1159] The system of the present invention not only allows users to easily obtain information on the purchase price and buyers of unwanted items, but also provides more appropriate information and optimizes the interface by taking into account the user's emotional state.

[1160] Overall system configuration

[1161] The system consists of the following main components:

[1162] 1. User Interface (UI): The input form for users to enter product information.

[1163] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[1164] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1165] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[1166] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[1167] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[1168] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[1169] User inputs and submits product information

[1170] 1. The user enters product information

[1171] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[1172] Example: "Brand bag, purchased in 2018, good condition"

[1173] 2. The device sends product information to the server

[1174] The terminal sends the product information entered by the user to the server using a secure communication protocol.

[1175] Data storage and purchase price calculation

[1176] 3. The server saves the product information to a database

[1177] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[1178] 4. The purchase price calculation module calculates the purchase price

[1179] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[1180] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[1181] Recommendations for buyers and disposal methods

[1182] 5. The buyer recommendation module recommends suitable buyers

[1183] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1184] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[1185] 6. The disposal method recommendation module suggests an appropriate disposal method.

[1186] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1187] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1188] Emotion Recognition and Interface Optimization

[1189] 7. Emotion engine recognizes the user's emotional state

[1190] The emotion engine analyzes the user's emotional state based on their operation and input, including the speed of their operation, frequency of their input, and text mining of their input.

[1191] Example: If a user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly.

[1192] 8. Emotional engine optimizes the interface

[1193] Based on the user's emotional state, the emotion engine changes the interface's appearance and operation, for example, providing more detailed instructions and reducing the number of steps required if the user is feeling anxious.

[1194] Sending and displaying results

[1195] 9. The server sends the calculation results and recommendation information to the device.

[1196] The server transmits the calculated purchase price, recommended purchaser information, or disposal method to the terminal.

[1197] 10. Display the information received by the device to the user

[1198] The terminal displays the received information, such as the approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[1199] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support tailored to their emotional state.

[1200] The processing flow will be explained below.

[1201] Step 1:

[1202] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[1203] Step 2:

[1204] The terminal transmits the product information entered by the user to the server using a secure communication protocol.

[1205] Step 3:

[1206] The server receives the product information and stores it in a database, properly recording information such as the product's brand name, category, purchase date, and condition.

[1207] Step 4:

[1208] The server's purchase price calculation module calculates the purchase price based on the product information stored in the database, referring to past purchase price data and market trends to calculate an approximate purchase price.

[1209] Step 5:

[1210] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and selects the most suitable buyer.

[1211] Step 6:

[1212] If the product has no value or no price, the server's disposal recommendation module searches for information on the appropriate disposal method, recycling, or donation destination.

[1213] Step 7:

[1214] The emotion engine analyzes the user's input and operational status to recognize their emotional state. For example, if the user is typing quickly, it will determine that they are feeling impatient.

[1215] Step 8:

[1216] An emotion engine dynamically changes the interface based on the user's perceived emotional state, for example simplifying instructions or using more friendly language for an impatient user.

[1217] Step 9:

[1218] The server then sends the calculated purchase price and recommended buyer information or disposal method to the terminal. This transmission is also carried out using a secure communication protocol.

[1219] Step 10:

[1220] The device displays the received information to the user, including an approximate purchase price, recommended buyers, or disposal methods, allowing the user to decide on the next course of action.

[1221] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and with the support of an emotion engine, they can receive the most appropriate information tailored to their situation.

[1222] Example 2

[1223] 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."

[1224] In conventional buying and selling systems, users can input product information and obtain information on buying prices and buyers, but the system does not take into account the user's emotional state when providing information or optimizing the interface, which can be stressful for users. Furthermore, the system also faces issues such as inefficiency and inaccuracy in calculating product buying prices and recommending buyers.

[1225] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1226] In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing the interface based on the emotional state, means for calculating an approximate purchase price based on product information, means for recommending a purchase service specialized for a specific brand or category, means for providing a method for disposing of priceless products, and means for transmitting the calculation results and recommendation information from the server to the user terminal. This allows the user to reduce stress while efficiently and accurately calculating the purchase price and receiving recommendations for buyers.

[1227] "User" refers to an individual or corporation that uses the system to request information on purchase prices and disposal methods for unwanted items.

[1228] "Product information" refers to detailed information about the unwanted product entered by the user, such as the brand name, category, purchase date, product condition, and image.

[1229] The "server" is a central control device that receives product information sent by users, processes it, and sends the results to the user terminal.

[1230] A "database" is an information management system that stores and manages data such as product information, past purchase price data, purchaser information, and market trends.

[1231] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on product information stored in a database, by referring to past purchase price data and market trends.

[1232] The "buyer recommendation module" is an algorithm or program that searches a database for the most suitable buyer based on product information and recommends it to the user.

[1233] A "disposal recommendation module" is an algorithm or program that provides users with information on appropriate disposal, recycling, or donation options for items that have no value.

[1234] An "emotion engine" is a program that analyzes the user's operation speed and input content to recognize their emotional state and reflect this in the system's operation.

[1235] A "user terminal" is a device (such as a smartphone or PC) that a user uses to input product information and receive and display the results from the server.

[1236] A "secure communication protocol" is a communication method that ensures security when sending and receiving data, such as HTTPS.

[1237] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides appropriate information and optimizes the interface by taking into account the user's emotional state.

[1238] Overall system configuration

[1239] The system consists of the following main components:

[1240] 1. User Interface (UI): The input form for users to enter product information.

[1241] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[1242] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1243] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[1244] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[1245] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[1246] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[1247] 8. User terminal: A device (e.g., a smartphone or PC) on which a user enters product information and receives and displays the results from the server.

[1248] User inputs and submits product information

[1249] The user uses a device to input product information into an input form on a dedicated application or website. This information includes the product brand name, category, purchase date, product condition, and an image (if necessary). For example, the user can input information such as "Brand bag, purchased in 2018, condition: good." The input product information is sent from the device to the server using a secure communication protocol (e.g., HTTPS).

[1250] Data storage and purchase price calculation

[1251] The server stores the received product information in a database. This storage process involves standard database operations (e.g., SQL queries) to maintain data integrity. Based on the stored product information, a purchase price calculation module calculates the purchase price. This module references past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, the calculation might be "brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1252] Recommendations for buyers and disposal methods

[1253] The buy-back recommendation module can search a database for the most suitable buy-back store based on product information and recommend it to the user. For example, it might recommend, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best." Furthermore, for items that have no value, the disposal recommendation module will suggest the appropriate disposal method to the user. For example, it might suggest, "This item is not eligible for purchase. We recommend that you take it to a recycling center or donate it to 'NPO C.'"

[1254] Emotion Recognition and Interface Optimization

[1255] The emotion engine analyzes the user's speed and input to recognize their emotional state. This analysis uses text mining and natural language processing (NLP). For example, if the user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly. Based on the emotional state, the emotion engine can change the UI layout to optimize the user experience.

[1256] Sending and displaying results

[1257] The server sends the calculated purchase price, recommended buyer information, and disposal method to the device, which then displays the received information to the user, allowing the user to decide on their next action based on the information obtained.

[1258] Example: A user enters "brand bag, purchased in 2018, condition: good," and the device sends this to the server. The server uses a purchase price calculation module to calculate an approximate price, and displays on the device, "The purchase price for this bag is 30,000 yen." The device also displays recommendation information such as, "The best places for this brand bag are 'Specialized Purchase Store A' and 'Online Purchase Service B.'"

[1259] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support based on their emotional state.

[1260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1261] Step 1:

[1262] User enters product information

[1263] Users use their devices to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and an image (if necessary).

[1264] Input: Manual input by the user

[1265] Output: Product information temporarily saved on the device

[1266] Specific action: For example, the user enters "brand bag, purchased in 2018, condition: good."

[1267] Step 2:

[1268] The device sends product information to the server

[1269] The terminal sends the entered product information to the server using a secure communication protocol (e.g., HTTPS).

[1270] Input: User-entered data

[1271] Output: Product information that arrives at the server

[1272] Specific operation: The terminal encrypts the product information using SSL / TLS and sends it to the server.

[1273] Step 3:

[1274] The server saves the product information to a database

[1275] The server stores the received product information in a database, which involves standard database operations (e.g., SQL queries) to ensure data integrity.

[1276] Input: Product information that arrives at the server

[1277] Output: Product information stored in a database

[1278] Specific behavior: Executes an SQL query to insert product information into the database. "INSERT INTO ProductInfo (BrandName, Category, PurchaseDate, Condition, Image) VALUES ('Brand Bag', 'Bag', '2018-01-01', 'Good', 'image_path');"

[1279] Step 4:

[1280] The purchase price calculation module calculates the purchase price

[1281] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[1282] Input: Product information stored in the database

[1283] Output: Calculated approximate purchase price

[1284] How it works: The buyback price calculation algorithm compares past buyback price data and market trend data, and then uses statistical analysis and machine learning models to predict prices. "It uses product information as model input and outputs an estimated price."

[1285] Step 5:

[1286] The buyer recommendation module recommends suitable buyers

[1287] The server's buyer recommendation module searches a database for buying services that specialize in the product brand or category, and recommends the most suitable buyer to the user.

[1288] Input: Product information stored in the database

[1289] Output: Recommended buyer list

[1290] Specific behavior: Executes an SQL query to search for buyback services for the relevant brand and category, and generates a list of the best buyback services. "SELECT FROM BuybackServices WHERE Category = 'Bags' AND Brand = 'Brand Bags';"

[1291] Step 6:

[1292] The disposal method recommendation module suggests appropriate disposal methods.

[1293] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1294] Input: Product information stored in the database

[1295] Output: List of recommended disposal methods

[1296] Specific behavior: Runs an SQL query to find and generate a list of disposal methods for the given category: SELECT FROM DisposalMethods WHERE Category = 'Bag';

[1297] Step 7:

[1298] Emotion engine recognizes the user's emotional state

[1299] The emotion engine analyzes the user's emotional state based on their operation and input, including operation speed, input frequency, and text mining of input content.

[1300] Input: User operation data and input data

[1301] Output: Perceived emotional state

[1302] Specific operation: Text mining is performed on the user's operation speed and input content, and the input is input into the emotion analysis model to output the emotion. "emotion_result = emotion_analysis_module.analyze(user_input)"

[1303] Step 8:

[1304] Emotional engine optimizes the interface

[1305] The emotion engine changes the look and feel of the interface based on the user's emotional state, for example by providing more detailed instructions or reducing the number of steps required.

[1306] Input: Perceived emotional state

[1307] Output: Optimized interface

[1308] What it does: The emotion engine dynamically changes UI components to display the appropriate message and layout for the user. "Sorry for the wait. I'll make this as easy as possible."

[1309] Step 9:

[1310] The server sends the calculation results and recommendation information to the device.

[1311] The server sends the calculated purchase price, recommended buyer information, and disposal method to the user's terminal.

[1312] Input: Purchase price, purchase destination list, disposal method list

[1313] Output: Calculation results and recommendations sent to the device

[1314] Specific operation: The server structures the result data through the API and sends it to the terminal. "The JSON format data is sent to the terminal via the API."

[1315] Step 10:

[1316] Displaying information received by the device to the user

[1317] The device displays the received information to the user in a way that is easy for the user to understand.

[1318] Input: Calculation results and recommendations received from the server

[1319] Output: Information displayed to the user

[1320] Specific operation: The device displays the purchase price and purchase destination information in a table format on the UI. "Purchase price: 30,000 yen Recommended purchase destinations: Specialized purchase store A, online purchase service B Disposal method: Recycling center D, donation to NPO C"

[1321] (Application example 2)

[1322] 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."

[1323] In conventional buy-back service systems, users often find it difficult to obtain information on the buy-back price and buyers of unwanted items, and the information provided does not reflect the user's emotional state, leaving many users dissatisfied. Furthermore, the lack of interface optimization that takes into account the user's emotional state results in a poor user experience.

[1324] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and optimizing the interface, means for the user to input product information, means for transmitting the product information to the server, means for the server to store the product information in a database, means for the server to calculate an approximate purchase price based on the product information stored in the database, means for the server to recommend a purchase service specialized in a specific brand or category, means for the server to provide a method for disposing of priceless products, means for transmitting the calculation results and recommended information from the server to a user terminal, and means for the user terminal to display the received information to the user. This enables users to easily obtain purchase prices and purchase destination information for unwanted products and to receive optimal support tailored to their emotional state.

[1325] "User interface" refers to the screen layout and operating means that allow users to input product information, etc.

[1326] "Server" means a central control device that receives, stores, processes data and provides information to users.

[1327] A "database" is an information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1328] A "purchase price calculation module" is software that includes an algorithm that calculates an approximate purchase price based on stored product information.

[1329] A "buyer recommendation module" is software that includes an algorithm that recommends appropriate buyers based on product information.

[1330] The "disposal recommendation module" is software that includes an algorithm that suggests appropriate disposal, recycling, or donation options for items that have no price tag.

[1331] An "emotion engine" is software that recognizes the user's emotional state and reflects it in the operation of the entire system.

[1332] "Emotional state" refers to the user's current psychological state, analyzed based on factors such as the user's operation speed, input frequency, and text mining of input content.

[1333] "Interface optimization" refers to changing the appearance and operation of an interface depending on the user's emotional state.

[1334] A "user terminal" is a device operated by a user, such as a smartphone.

[1335] "Product information" refers to data entered by the user, such as the product brand name, category, purchase date, product condition, and images.

[1336] The "calculation result" refers to the approximate purchase price calculated by the purchase price calculation module.

[1337] "Recommendation information" is information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module.

[1338] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides optimal support according to the user's emotional state. The overall configuration of this system is as follows.

[1339] Overall system configuration

[1340] 1. User Interface (UI)

[1341] Users use a smartphone application to input product information, which has a function to scan barcodes and QR codes and automatically input the scanned product information.

[1342] 2. Emotion Engine

[1343] The system analyzes the user's emotional state based on their input speed and operation method. It uses a generative AI model for emotion analysis and determines the user's emotional state through text mining of the input content.

[1344] 3. Server

[1345] Product information storage: Product information sent by users is saved on the server and stored in a database. Product information includes brand name, category, purchase date, product condition, images, etc.

[1346] Calculation of purchase price: The purchase price calculation module on the server calculates the purchase price based on the product information stored in the database. The calculation is performed based on past purchase price data for similar products and market trends.

[1347] Buyer recommendation: The buy-back recommendation module on the server searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back service to the user.

[1348] Providing disposal methods: For items that are not eligible for purchase, a disposal recommendation module on the server will suggest recycling methods or donation destinations to users.

[1349] 4. User Device

[1350] The system receives calculation results and recommendation information sent from the server and displays them to the user. It also optimizes the interface according to the user's emotional state. For example, if the user is feeling anxious, it will display more detailed explanations to reassure them.

[1351] Hardware and software used

[1352] Hardware

[1353] Smartphone camera (for scanning product information)

[1354] Server (for data processing and storage)

[1355] software

[1356] OpenCV (for camera operation and image processing)

[1357] TensorFlow / Keras (for emotion recognition models)

[1358] Transformers (generative AI models for sentiment analysis)

[1359] Python standard library (for data processing and communication control)

[1360] Specific examples

[1361] The specific steps for a user to scan a brand-name bag they no longer use at home with the app and find out the purchase price are as follows:

[1362] 1. The user scans the bag's barcode with their smartphone camera.

[1363] 2. The scanned product information is sent to the server.

[1364] 3. The server calculates the purchase price and recommends the optimal buyer and disposal method.

[1365] 4. The emotion engine analyzes the user's emotional state based on their input.

[1366] 5. The interface is optimized according to the user's emotional state, and calculation results and recommendation information are displayed on the user's device.

[1367] Prompt Sentence Examples

[1368] "Please enter designer bag"

[1369] "Please enter product information: brand name, category, year of purchase, condition"

[1370] "Scan the product with your camera"

[1371] "How are you feeling right now? (free input)"

[1372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1373] Step 1:

[1374] The user scans a product with the smartphone camera. The user launches the application and uses the camera function to read the product's barcode or QR code. The image data is taken as input, and the barcode information is analyzed using OpenCV. Product information (brand name, category, year of purchase, condition) is obtained as output.

[1375] Step 2:

[1376] The terminal transmits the acquired product information to the server. The terminal structures the product information and transmits it to the server using a secure communication protocol. The terminal receives product information as input and transmits it to the server as output.

[1377] Step 3:

[1378] The server receives the product information and stores it in a database. The server stores the received product information in a database and performs standard database operations to maintain data integrity. The server receives product information as input and stores it in a database as output.

[1379] Step 4:

[1380] The server's purchase price calculation module calculates the purchase price based on the information in the database. The server calculates the purchase price based on the stored product information, referring to past purchase price data for similar products and market trends. It receives product information and market data as input and calculates an approximate purchase price as output.

[1381] Step 5:

[1382] The server's buyback recommendation module searches for the most suitable buyback service. The server searches the database for buyback services that specialize in specific brands or categories and recommends the most suitable buyback service. It receives product information as input and creates a list of recommended buyback services as output.

[1383] Step 6:

[1384] The server's disposal recommendation module provides appropriate disposal methods. For items that are not eligible for purchase, the server suggests appropriate recycling methods or donation information to the user. It receives product information as input and creates a list of disposal methods as output.

[1385] Step 7:

[1386] The server's emotion engine analyzes the user's emotional state. It uses a generative AI model to analyze the emotional state based on data such as the user's free description and operation speed. It receives the user's operation data as input and obtains the user's emotional state as output.

[1387] Step 8:

[1388] The server optimizes the interface according to the emotional state and sends the calculation results and recommendation information to the terminal.The server takes the user's emotional state into consideration and optimizes the interface in simple or detailed form, then sends the calculation results and recommendation information to the terminal.The server receives the emotional state, calculation results, and recommendation information as input, and sends the optimized information to the user's terminal as output.

[1389] Step 9:

[1390] The terminal displays the received information to the user. The terminal displays the calculation results, recommendation information, and optimized interface sent from the server to the user. It receives information from the server as input and visualizes and presents it to the user as output.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] [Fourth embodiment]

[1395] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1396] 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.

[1397] 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).

[1398] 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.

[1399] 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.

[1400] 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).

[1401] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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."

[1408] The system of the present invention supports efficient decluttering by allowing users to easily obtain information on the purchase prices and buyers of unwanted items.

[1409] Overall system configuration

[1410] The system consists of the following main components:

[1411] 1. User Interface (UI): The input form for users to enter product information.

[1412] 2. Server: A central control unit that receives input data from users, stores it in a database, and performs calculations and searches.

[1413] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1414] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[1415] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[1416] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[1417] User inputs and submits product information

[1418] 1. The user enters product information

[1419] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[1420] Example: "Brand bag, purchased in 2018, good condition"

[1421] 2. The device sends product information to the server

[1422] The terminal transmits the collected product information to the server using a secure communication protocol.

[1423] Data storage and purchase price calculation

[1424] 3. The server saves the product information to a database

[1425] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[1426] 4. The purchase price calculation module calculates the purchase price

[1427] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[1428] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[1429] Recommendations for buyers and disposal methods

[1430] 5. The buyer recommendation module recommends suitable buyers

[1431] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1432] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[1433] 6. The disposal method recommendation module suggests an appropriate disposal method.

[1434] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1435] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1436] Sending and displaying results

[1437] 7. The server sends the calculation results and recommendation information to the device.

[1438] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[1439] 8. Display the information received by the device to the user

[1440] The terminal displays the received information, i.e., approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[1441] The system allows users to obtain specific information about unnecessary items in their homes and efficiently progress through the decluttering process.

[1442] The processing flow will be explained below.

[1443] Step 1:

[1444] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[1445] Step 2:

[1446] The terminal sends the product information entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[1447] Step 3:

[1448] The server stores the received product information in a database, carefully recording attributes such as the product's brand name, category, purchase date, and condition.

[1449] Step 4:

[1450] The server's purchase price calculation module starts processing based on the product information stored in the database. The server calculates an approximate purchase price by referencing past purchase price data and market trend data stored in the database.

[1451] Step 5:

[1452] The server's buyback recommendation module searches the database for buyback services that specialize in specific brands or categories based on product information, and then lists the most suitable buyback services from the search results.

[1453] Step 6:

[1454] If the product has no resale value, the server's disposal recommendation module will suggest disposal options and allow the user to receive information about nearby recycling centers or donation locations.

[1455] Step 7:

[1456] The server sends the purchase price calculation results and recommended buyer information or disposal method to the terminal using a real-time communication protocol.

[1457] Step 8:

[1458] The device displays the received information to the user, who can then decide on their next course of action based on the approximate purchase price, recommended buyers, or disposal methods displayed.

[1459] This process allows users to easily obtain information on the purchase price and buyers of unwanted items from the comfort of their own home, making decluttering a smooth process.

[1460] Example 1

[1461] 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."

[1462] In today's consumer society, many users are looking for efficient ways to dispose of unwanted items. However, it is difficult and time-consuming to find out the purchase price for each item, the best buyer, and the appropriate disposal method for priceless items. Therefore, there is a need for a way for users to easily obtain information on the purchase price and buyers of unwanted items, as well as the appropriate disposal method for priceless items.

[1463] 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.

[1464] In this invention, the server includes means for a user to input product information, means for transmitting the product information to the server, means for storing the product information in a database, means for calculating an approximate purchase price, means for recommending a purchase service specialized in a specific brand or category, means for providing a method for disposing of priceless products, means for transmitting the calculation results and the recommended information to a user terminal, means for displaying information received by the user terminal to the user, means for transmitting and receiving the product information using a secure communication protocol, means for predicting a purchase price using a machine learning model, and means for acquiring recycling information using an external API. This allows users to easily find out the specific purchase prices and purchase locations for unwanted products, as well as appropriate disposal methods, from the comfort of their own home.

[1465] "User" refers to an individual or organization that uses the system to input product information and obtain purchase prices and purchase destination information.

[1466] "Product Information" means detailed data about a product provided by a user, such as the product brand name, category, purchase date, product condition, and, if necessary, product images.

[1467] The "server" is a computer system that serves as the core of the entire system, receiving and processing product information, calculating purchase prices, recommending buyers, and providing disposal methods.

[1468] A "database" is an information management system that allows a server to store and manage product information, past purchase price data, market trends, purchaser information, and so on.

[1469] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on the product information stored in the database.

[1470] The "Buyback Recommendation Module" is an algorithm or program that recommends buyback services that specialize in specific brands or categories based on product information.

[1471] A "disposal recommendation module" is an algorithm or program that provides information on appropriate disposal methods, recycling, and donation destinations for items that have no price tag.

[1472] A "terminal" is a device such as a smartphone or PC that a user uses to input product information and receive and display calculation results and recommendation information from the server.

[1473] A "secure communication protocol" is a means of securely communicating data between a terminal and a server, and generally includes HTTPS.

[1474] A "machine learning model" is an artificial intelligence algorithm that predicts the purchase price of a product based on past data in a database and market trends.

[1475] "External API" means an external program interface used to obtain recycling information and other related information.

[1476] "Purchase price" is the estimated value of the unwanted item calculated by the purchase price calculation module of the server.

[1477] "Recommended information" refers to information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module of the server.

[1478] This invention relates to a system that allows users to easily obtain information on the purchase price and buyers of unwanted items. The main components of this system are a user interface, a server, a database, a purchase price calculation module, a buyer recommendation module, and a disposal method recommendation module.

[1479] First, the user enters product information using a dedicated application or website. The device used is a smartphone or PC, and the information is entered using this device. The information entered by the user includes the product brand name, category, purchase date, product condition, and, if necessary, an image. For example, the user might enter "brand bag, purchased in 2018, condition: good."

[1480] Next, the device sends the product information to the server. This communication uses a secure communication protocol (such as HTTPS). The communication module in the device calls the API endpoint and sends an HTTP request containing the product information to the server.

[1481] The server stores the received product information in a database. Databases such as MySQL or PostgreSQL are used as the database. A database management program on the server executes an "INSERT INTO" query to store the product information. Based on the stored product information, the server's purchase price calculation module uses a machine learning model (such as TensorFlow) to refer to past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1482] Next, the server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. A Flask application is used to search for buy-back stores, and the obtained buy-back store information is organized and formatted for display. For example, it might say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[1483] Furthermore, if the item has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation. To do this, the Node.js program uses an external API to obtain recycling information and compares it with information stored in the database to determine the optimal disposal method. For example, it might say, "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1484] The server then sends the calculated purchase price and recommended buyer information to the device, which then displays this information to the user. Client-side scripts such as JavaScript are used to parse the received data and dynamically update the information in HTML or in the display area within the app. This allows the user to obtain specific information about the purchase price, buyer, and disposal method, and decide on their next course of action.

[1485] As a concrete example, the prompt sentence is shown below.

[1486] Example prompt sentence:

[1487] "Please tell me the details of the program that calculates the purchase price."

[1488] "Please explain the algorithm that recommends the best buyers."

[1489] "What are the steps for recycling products and recommending donation destinations?"

[1490] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[1491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1492] Step 1:

[1493] The user enters product information.

[1494] The user enters the product brand name, category, purchase date, product condition, and, if necessary, an image into an input form on a dedicated application or website. The entered information is internally converted into JSON or form data format. For example, the user enters product information such as "Brand bag, purchased in 2018, condition: good" and clicks the "Submit" button. Input: Detailed product information. Output: Aggregation of input data.

[1495] Step 2:

[1496] The terminal transmits the product information to the server.

[1497] The device sends the stored product information to the server using the HTTPS protocol. The communication module calls the API endpoint and sends a POST request. Input: Product information stored in the device. Output: Request to the server. Specifically, the data is encrypted and divided into packets before being sent.

[1498] Step 3:

[1499] The server stores the product information in a database.

[1500] The server analyzes the product information received from the terminal and saves it in a database. MySQL or PostgreSQL is used for the database. The database management program on the server executes an "INSERT INTO" query to save the product information. Input: Received product information. Output: Results saved in the database. Specifically, data validation is performed to confirm consistency.

[1501] Step 4:

[1502] The purchase price calculation module calculates the purchase price.

[1503] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database. Input data includes past purchase price data for similar products and market trends. A TensorFlow model is used to predict the price, and the results are saved in a temporary data area. Input: Product information in the database. Output: Estimated purchase price. For example, "Brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1504] Step 5:

[1505] The buyer recommendation module recommends suitable buyers.

[1506] The server's buy-back store recommendation module searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back store to the user. The Flask application executes the search query and organizes and formats the buy-back store information obtained. Input: Buy-back store information in the database. Output: Recommended buy-back stores. For example, it would say, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best choices."

[1507] Step 6:

[1508] The disposal method recommendation module suggests an appropriate disposal method.

[1509] If the product has no value, the server's disposal recommendation module provides the user with information on appropriate disposal methods, recycling, or donation destinations. The Node.js program retrieves recycling information from an external API and selects the optimal disposal method. Input: Product worthlessness information and recycling API information. Output: Recommended disposal method. A specific example would be, "This product is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1510] Step 7:

[1511] The server sends the calculation results and recommendation information to the terminal.

[1512] The server sends the calculated purchase price, recommended buyer information, and disposal method to the terminal. Data is again sent securely using the HTTPS protocol. Input: Calculation results and recommended information. Output: Data sent to the terminal. The specific operation is to generate an HTTP response.

[1513] Step 8:

[1514] The device displays the received information to the user.

[1515] The device analyzes the received information and displays it to the user. A client-side script such as JavaScript or React parses the data and dynamically displays it on the screen. Input: Data received from the server. Output: Displayed on the user interface. For example, the information displayed might be "Purchase price: 30,000 yen, Recommended purchase locations: Specialized purchase store A, Online purchase service B."

[1516] This allows users to easily find out the specific purchase price, buyers, and appropriate disposal methods for unwanted items from the comfort of their own home.

[1517] (Application example 1)

[1518] 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."

[1519] Conventional systems for buying unwanted items have the problem that users have to input product information themselves, and then use that information to search for the appropriate buyer, which is time-consuming. Additionally, in certain physical stores, it can be difficult for staff to quickly calculate product purchase prices and provide appropriate recommendations to customers. This makes it difficult to achieve efficient decluttering, and further improvements in convenience are needed.

[1520] 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.

[1521] In this invention, the server includes: means for a user to input product information; means for transmitting the product information to the server; means for the server to store the product information in a database; means for the server to calculate an approximate purchase price based on the product information stored in the database; means for the server to recommend a purchase service specialized in a specific brand or category; means for the server to provide a disposal method for priceless products; means for transmitting the calculation results and recommended information from the server to a user terminal; means for the user terminal to display the received information to the user; means including a terminal for a physical store where customers or staff enter product information; means for inputting a prompt sentence into a generative AI model; and means for obtaining a response from the generative AI model based on the prompt sentence. This automates the entire process from inputting product information to calculating a purchase price and recommending an appropriate buyer, significantly reducing the workload of users and staff and enabling efficient decluttering.

[1522] "Means for users to input product information" refers to interfaces and devices (kiosks, tablets, smart glasses, etc.) that allow customers or staff to input data about products (such as brand name, category, date of purchase, product condition, and images).

[1523] The "means for transmitting the product information to the server" refers to a function and program for transmitting the product information entered by the customer or staff member to the server using a secure communication protocol.

[1524] The "means for the server to store the product information in a database" refers to the processes and techniques for storing the product information received by the server in a database and maintaining consistency and completeness.

[1525] "Means for the server to calculate an approximate purchase price based on the product information stored in the database" refers to the function by which the server applies an algorithm based on the information in the database and market trends to calculate an estimated purchase price for the product.

[1526] "Means for the server to recommend buy-back services specialized in specific brands or categories" refers to a function that searches the database for buy-back services corresponding to specific brands or categories and suggests the most suitable buy-back service to the user.

[1527] "Means for the server to provide methods for disposing of priceless items" refers to a function that provides users with information on appropriate disposal methods, recycling, and donation destinations for items that are not eligible for purchase.

[1528] "Means for transmitting calculation results and recommendation information from the server to the user terminal" refers to a function for transmitting the purchase price and recommendation information calculated by the server to the terminal used by the user via a secure communication protocol.

[1529] "Means for displaying to the user the information received by the user terminal" refers to an interface and program for displaying to the user the purchase price and recommendation information received by the user terminal in an easy-to-see manner.

[1530] "Means including terminals for physical stores where customers or staff can input product information" refers to devices and functions that allow customers to input and submit product information, such as kiosk terminals installed in physical stores or mobile devices used by staff.

[1531] "Means for inputting prompt text into the generative AI model" refers to the interface and functions that allow users and staff to input product information and instructions in text form into the generative AI model.

[1532] "Means for obtaining a response from the generative AI model based on the prompt sentence" refers to a function that enables the server to obtain a response generated in response to a prompt input to the generative AI model.

[1533] The system of the present invention is designed to automate and streamline the process of buying unwanted items in brick-and-mortar stores. The basic components are a user interface (UI), a server, a database (DB), a buyback price calculation module, a buyback store recommendation module, a disposal method recommendation module, and a generative AI model.

[1534] User inputs and submits product information

[1535] 1. The user enters product information

[1536] Using devices such as kiosks in physical stores or tablets or smart glasses used by staff, users or staff enter product information, including the product brand name, category, purchase date, product condition, and images (if applicable).

[1537] 2. The device sends product information to the server

[1538] The terminal transmits the collected product information to the server using a secure communication protocol (e.g., HTTPS).

[1539] Data storage and purchase price calculation

[1540] 3. The server saves the product information to a database

[1541] Once the server receives the product information, it stores it in a database (e.g., MySQL, PostgreSQL). This process involves standard database operations to ensure data integrity.

[1542] 4. The purchase price calculation module calculates the purchase price

[1543] The purchase price calculation module on the server calculates an approximate purchase price based on product information stored in the database, referring to past purchase price data for similar products and market trends. This module is implemented in a programming language such as Python.

[1544] Recommendations for buyers and disposal methods

[1545] 5. The buyer recommendation module recommends suitable buyers

[1546] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1547] 6. The disposal method recommendation module suggests an appropriate disposal method.

[1548] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1549] Using generative AI models

[1550] 7. Enter a prompt into the generative AI model

[1551] The server generates a prompt sentence based on the input product information and inputs it into a generative AI model (e.g., GPT-3).

[1552] 8. Obtaining responses from generative AI models

[1553] The response generated by the generative AI model is obtained, and the final purchase price and recommendation information are determined based on that content.

[1554] Sending and displaying results

[1555] 9. The server sends the calculation results and recommendation information to the device.

[1556] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[1557] 10. Display the information received by the device to the user

[1558] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[1559] Adding specific examples

[1560] For example, if a customer enters product information such as "Brand bag, purchased in 2018, condition: good," the following prompt sentence is input into the generative AI model.

[1561] Example prompt sentence:

[1562] This is a designer bag, purchased in 2018, in good condition. Please let me know the purchase price based on market trends. Also, please recommend any stores or online services that will buy this item.

[1563] Based on this prompt, the generative AI model calculates the purchase price and recommends an appropriate buyer, and the information obtained is sent back to the server and ultimately displayed on the user's or staff's device.

[1564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1565] Step 1:

[1566] A user or staff member enters product information.

[1567] The terminal provides a function for users or staff to input data about products (brand name, category, purchase date, product condition, image, etc.) through an interface.

[1568] Input: Product information (brand name, category, purchase date, condition, image)

[1569] Output: Input product information data

[1570] Step 2:

[1571] Product information is sent to the server.

[1572] The terminal transmits the input product information to the server using a secure protocol (for example, HTTPS).

[1573] Input: Entered product information data

[1574] Output: Product information sent to the server

[1575] Step 3:

[1576] Store product information in a database.

[1577] The server stores the received product information in a database (MySQL or PostgreSQL), performing appropriate database operations to maintain data integrity and consistency.

[1578] Input: Product information sent to the server

[1579] Output: Product information stored in the database

[1580] Step 4:

[1581] Calculate the purchase price.

[1582] The server's purchase price calculation module calculates an approximate purchase price based on product information stored in the database, market trends, and past purchase price data.

[1583] Input: Product information stored in the database, past purchase price data, market trend data

[1584] Output: Approximate purchase price

[1585] Step 5:

[1586] Recommend suitable buyers.

[1587] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1588] Input: Product information, purchase price, and purchase service data stored in the database

[1589] Output: Recommended buyer information

[1590] Step 6:

[1591] Propose appropriate disposal methods.

[1592] The server's disposal recommendation module provides users with information on appropriate disposal, recycling, and donation options for items that have no value or are otherwise invaluable.

[1593] Input: Product information stored in the database

[1594] Output: Recommended disposal, recycling, or donation information

[1595] Step 7:

[1596] Input a prompt sentence into the generative AI model.

[1597] The server generates a prompt sentence based on the input product information and inputs it into the generative AI model.

[1598] Input: Product information, prompt text

[1599] Output: The prompt sent to the generative AI model

[1600] Step 8:

[1601] Obtain a response from the generative AI model.

[1602] The server obtains the response generated by the generative AI model and determines the final purchase price and recommendation information based on the content.

[1603] Input: Response from a generative AI model

[1604] Output: Final purchase price, recommendation information

[1605] Step 9:

[1606] The calculation results and recommendation information are transmitted to the terminal.

[1607] The server transmits the calculated purchase price and recommended buyer information to the terminal.

[1608] Input: Final purchase price, recommendation information

[1609] Output: Buy price and recommendation information sent to the terminal

[1610] Step 10:

[1611] Display the received information to the user.

[1612] The terminal displays the received information, i.e., the approximate purchase price, recommended buyers, or disposal methods, to the user.

[1613] Input: Purchase price and recommendation information sent to the terminal

[1614] Output: Buy price and recommendation displayed to user

[1615] 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.

[1616] The system of the present invention not only allows users to easily obtain information on the purchase price and buyers of unwanted items, but also provides more appropriate information and optimizes the interface by taking into account the user's emotional state.

[1617] Overall system configuration

[1618] The system consists of the following main components:

[1619] 1. User Interface (UI): The input form for users to enter product information.

[1620] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[1621] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1622] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[1623] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[1624] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[1625] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[1626] User inputs and submits product information

[1627] 1. The user enters product information

[1628] Users use their devices (smartphones or PCs) to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and images (if necessary).

[1629] Example: "Brand bag, purchased in 2018, good condition"

[1630] 2. The device sends product information to the server

[1631] The terminal sends the product information entered by the user to the server using a secure communication protocol.

[1632] Data storage and purchase price calculation

[1633] 3. The server saves the product information to a database

[1634] After receiving the product information, the server stores it in a database. This process involves standard database operations to ensure data integrity.

[1635] 4. The purchase price calculation module calculates the purchase price

[1636] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[1637] Example: "Brand bag, purchased in 2018, in good condition → Approximate purchase price: 30,000 yen"

[1638] Recommendations for buyers and disposal methods

[1639] 5. The buyer recommendation module recommends suitable buyers

[1640] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and recommends the most suitable buyer to the user.

[1641] Example: "For this designer bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best options."

[1642] 6. The disposal method recommendation module suggests an appropriate disposal method.

[1643] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1644] Example: "This item is not eligible for purchase. We recommend taking it to a recycling center or donating it to NPO C."

[1645] Emotion Recognition and Interface Optimization

[1646] 7. Emotion engine recognizes the user's emotional state

[1647] The emotion engine analyzes the user's emotional state based on their operation and input, including the speed of their operation, frequency of their input, and text mining of their input.

[1648] Example: If a user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly.

[1649] 8. Emotional engine optimizes the interface

[1650] Based on the user's emotional state, the emotion engine changes the interface's appearance and operation, for example, providing more detailed instructions and reducing the number of steps required if the user is feeling anxious.

[1651] Sending and displaying results

[1652] 9. The server sends the calculation results and recommendation information to the device.

[1653] The server transmits the calculated purchase price, recommended purchaser information, or disposal method to the terminal.

[1654] 10. Display the information received by the device to the user

[1655] The terminal displays the received information, such as the approximate purchase price, recommended buyers, or disposal methods, to the user, who then decides on the next steps.

[1656] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support tailored to their emotional state.

[1657] The processing flow will be explained below.

[1658] Step 1:

[1659] The user enters product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and, if necessary, product images.

[1660] Step 2:

[1661] The terminal transmits the product information entered by the user to the server using a secure communication protocol.

[1662] Step 3:

[1663] The server receives the product information and stores it in a database, properly recording information such as the product's brand name, category, purchase date, and condition.

[1664] Step 4:

[1665] The server's purchase price calculation module calculates the purchase price based on the product information stored in the database, referring to past purchase price data and market trends to calculate an approximate purchase price.

[1666] Step 5:

[1667] The server's buyer recommendation module searches the database for buying services that specialize in specific brands or categories, and selects the most suitable buyer.

[1668] Step 6:

[1669] If the product has no value or no price, the server's disposal recommendation module searches for information on the appropriate disposal method, recycling, or donation destination.

[1670] Step 7:

[1671] The emotion engine analyzes the user's input and operational status to recognize their emotional state. For example, if the user is typing quickly, it will determine that they are feeling impatient.

[1672] Step 8:

[1673] An emotion engine dynamically changes the interface based on the user's perceived emotional state, for example simplifying instructions or using more friendly language for an impatient user.

[1674] Step 9:

[1675] The server then sends the calculated purchase price and recommended buyer information or disposal method to the terminal. This transmission is also carried out using a secure communication protocol.

[1676] Step 10:

[1677] The device displays the received information to the user, including an approximate purchase price, recommended buyers, or disposal methods, allowing the user to decide on the next course of action.

[1678] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and with the support of an emotion engine, they can receive the most appropriate information tailored to their situation.

[1679] Example 2

[1680] 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."

[1681] In conventional buying and selling systems, users can input product information and obtain information on buying prices and buyers, but the system does not take into account the user's emotional state when providing information or optimizing the interface, which can be stressful for users. Furthermore, the system also faces issues such as inefficiency and inaccuracy in calculating product buying prices and recommending buyers.

[1682] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1683] In this invention, the server includes means for analyzing the emotional state of the user, means for optimizing the interface based on the emotional state, means for calculating an approximate purchase price based on product information, means for recommending a purchase service specialized for a specific brand or category, means for providing a method for disposing of priceless products, and means for transmitting the calculation results and recommendation information from the server to the user terminal. This allows the user to reduce stress while efficiently and accurately calculating the purchase price and receiving recommendations for buyers.

[1684] "User" refers to an individual or corporation that uses the system to request information on purchase prices and disposal methods for unwanted items.

[1685] "Product information" refers to detailed information about the unwanted product entered by the user, such as the brand name, category, purchase date, product condition, and image.

[1686] The "server" is a central control device that receives product information sent by users, processes it, and sends the results to the user terminal.

[1687] A "database" is an information management system that stores and manages data such as product information, past purchase price data, purchaser information, and market trends.

[1688] The "purchase price calculation module" is an algorithm or program that calculates an approximate purchase price based on product information stored in a database, by referring to past purchase price data and market trends.

[1689] The "buyer recommendation module" is an algorithm or program that searches a database for the most suitable buyer based on product information and recommends it to the user.

[1690] A "disposal recommendation module" is an algorithm or program that provides users with information on appropriate disposal, recycling, or donation options for items that have no value.

[1691] An "emotion engine" is a program that analyzes the user's operation speed and input content to recognize their emotional state and reflect this in the system's operation.

[1692] A "user terminal" is a device (such as a smartphone or PC) that a user uses to input product information and receive and display the results from the server.

[1693] A "secure communication protocol" is a communication method that ensures security when sending and receiving data, such as HTTPS.

[1694] The system of the present invention allows users to easily obtain information on the purchase price and buyers of unwanted items, and also provides appropriate information and optimizes the interface by taking into account the user's emotional state.

[1695] Overall system configuration

[1696] The system consists of the following main components:

[1697] 1. User Interface (UI): The input form for users to enter product information.

[1698] 2. Server: A central control device that receives input data from users, stores it in a database, calculates the purchase price, and recommends buyers.

[1699] 3. Database (DB): An information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1700] 4. Purchase price calculation module: A module that has an algorithm that calculates an approximate purchase price based on the stored product information.

[1701] 5. Buyer recommendation module: A module that recommends the most suitable buyer based on product information.

[1702] 6. Disposal method recommendation module: A module that suggests appropriate disposal methods for items that cannot be priced.

[1703] 7. Emotion Engine: A module that recognizes the user's emotional state and influences the behavior of the entire system.

[1704] 8. User terminal: A device (e.g., a smartphone or PC) on which a user enters product information and receives and displays the results from the server.

[1705] User inputs and submits product information

[1706] The user uses a device to input product information into an input form on a dedicated application or website. This information includes the product brand name, category, purchase date, product condition, and an image (if necessary). For example, the user can input information such as "Brand bag, purchased in 2018, condition: good." The input product information is sent from the device to the server using a secure communication protocol (e.g., HTTPS).

[1707] Data storage and purchase price calculation

[1708] The server stores the received product information in a database. This storage process involves standard database operations (e.g., SQL queries) to maintain data integrity. Based on the stored product information, a purchase price calculation module calculates the purchase price. This module references past purchase price data for similar products and market trends to calculate an approximate purchase price. For example, the calculation might be "brand bag, purchased in 2018, in good condition → approximate purchase price is 30,000 yen."

[1709] Recommendations for buyers and disposal methods

[1710] The buy-back recommendation module can search a database for the most suitable buy-back store based on product information and recommend it to the user. For example, it might recommend, "For this brand bag, 'Specialized Buy-Back Store A' and 'Online Buy-Back Service B' are the best." Furthermore, for items that have no value, the disposal recommendation module will suggest the appropriate disposal method to the user. For example, it might suggest, "This item is not eligible for purchase. We recommend that you take it to a recycling center or donate it to 'NPO C.'"

[1711] Emotion Recognition and Interface Optimization

[1712] The emotion engine analyzes the user's speed and input to recognize their emotional state. This analysis uses text mining and natural language processing (NLP). For example, if the user is frustrated, the emotion engine can detect this and simplify the interface or change the language to be more user-friendly. Based on the emotional state, the emotion engine can change the UI layout to optimize the user experience.

[1713] Sending and displaying results

[1714] The server sends the calculated purchase price, recommended buyer information, and disposal method to the device, which then displays the received information to the user, allowing the user to decide on their next action based on the information obtained.

[1715] Example: A user enters "brand bag, purchased in 2018, condition: good," and the device sends this to the server. The server uses a purchase price calculation module to calculate an approximate price, and displays on the device, "The purchase price for this bag is 30,000 yen." The device also displays recommendation information such as, "The best places for this brand bag are 'Specialized Purchase Store A' and 'Online Purchase Service B.'"

[1716] This system allows users to easily obtain information on purchase prices and buyers for unwanted items from the comfort of their own home, and also allows them to smoothly proceed with decluttering while receiving optimal support based on their emotional state.

[1717] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1718] Step 1:

[1719] User enters product information

[1720] Users use their devices to enter product information into a dedicated application or website input form, including the product brand name, category, purchase date, product condition, and an image (if necessary).

[1721] Input: Manual input by the user

[1722] Output: Product information temporarily saved on the device

[1723] Specific action: For example, the user enters "brand bag, purchased in 2018, condition: good."

[1724] Step 2:

[1725] The device sends product information to the server

[1726] The terminal sends the entered product information to the server using a secure communication protocol (e.g., HTTPS).

[1727] Input: User-entered data

[1728] Output: Product information that arrives at the server

[1729] Specific operation: The terminal encrypts the product information using SSL / TLS and sends it to the server.

[1730] Step 3:

[1731] The server saves the product information to a database

[1732] The server stores the received product information in a database, which involves standard database operations (e.g., SQL queries) to ensure data integrity.

[1733] Input: Product information that arrives at the server

[1734] Output: Product information stored in a database

[1735] Specific behavior: Executes an SQL query to insert product information into the database. "INSERT INTO ProductInfo (BrandName, Category, PurchaseDate, Condition, Image) VALUES ('Brand Bag', 'Bag', '2018-01-01', 'Good', 'image_path');"

[1736] Step 4:

[1737] The purchase price calculation module calculates the purchase price

[1738] The server's purchase price calculation module calculates an approximate purchase price based on the product information stored in the database, by referring to past purchase price data for similar products and market trends.

[1739] Input: Product information stored in the database

[1740] Output: Calculated approximate purchase price

[1741] How it works: The buyback price calculation algorithm compares past buyback price data and market trend data, and then uses statistical analysis and machine learning models to predict prices. "It uses product information as model input and outputs an estimated price."

[1742] Step 5:

[1743] The buyer recommendation module recommends suitable buyers

[1744] The server's buyer recommendation module searches a database for buying services that specialize in the product brand or category, and recommends the most suitable buyer to the user.

[1745] Input: Product information stored in the database

[1746] Output: Recommended buyer list

[1747] Specific behavior: Executes an SQL query to search for buyback services for the relevant brand and category, and generates a list of the best buyback services. "SELECT FROM BuybackServices WHERE Category = 'Bags' AND Brand = 'Brand Bags';"

[1748] Step 6:

[1749] The disposal method recommendation module suggests appropriate disposal methods.

[1750] If the item has no value or cannot be priced, the server's disposal recommendation module will provide the user with information on appropriate disposal, recycling, or donation options.

[1751] Input: Product information stored in the database

[1752] Output: List of recommended disposal methods

[1753] Specific behavior: Runs an SQL query to find and generate a list of disposal methods for the given category: SELECT FROM DisposalMethods WHERE Category = 'Bag';

[1754] Step 7:

[1755] Emotion engine recognizes the user's emotional state

[1756] The emotion engine analyzes the user's emotional state based on their operation and input, including operation speed, input frequency, and text mining of input content.

[1757] Input: User operation data and input data

[1758] Output: Perceived emotional state

[1759] Specific operation: Text mining is performed on the user's operation speed and input content, and the input is input into the emotion analysis model to output the emotion. "emotion_result = emotion_analysis_module.analyze(user_input)"

[1760] Step 8:

[1761] Emotional engine optimizes the interface

[1762] The emotion engine changes the look and feel of the interface based on the user's emotional state, for example by providing more detailed instructions or reducing the number of steps required.

[1763] Input: Perceived emotional state

[1764] Output: Optimized interface

[1765] What it does: The emotion engine dynamically changes UI components to display the appropriate message and layout for the user. "Sorry for the wait. I'll make this as easy as possible."

[1766] Step 9:

[1767] The server sends the calculation results and recommendation information to the device.

[1768] The server sends the calculated purchase price, recommended buyer information, and disposal method to the user's terminal.

[1769] Input: Purchase price, purchase destination list, disposal method list

[1770] Output: Calculation results and recommendations sent to the device

[1771] Specific operation: The server structures the result data through the API and sends it to the terminal. "The JSON format data is sent to the terminal via the API."

[1772] Step 10:

[1773] Displaying information received by the device to the user

[1774] The device displays the received information to the user in a way that is easy for the user to understand.

[1775] Input: Calculation results and recommendations received from the server

[1776] Output: Information displayed to the user

[1777] Specific operation: The device displays the purchase price and purchase destination information in a table format on the UI. "Purchase price: 30,000 yen Recommended purchase destinations: Specialized purchase store A, online purchase service B Disposal method: Recycling center D, donation to NPO C"

[1778] (Application example 2)

[1779] 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."

[1780] In conventional buy-back service systems, users often find it difficult to obtain information on the buy-back price and buyers of unwanted items, and the information provided does not reflect the user's emotional state, leaving many users dissatisfied. Furthermore, the lack of interface optimization that takes into account the user's emotional state results in a poor user experience.

[1781] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state of the user and optimizing the interface, means for the user to input product information, means for transmitting the product information to the server, means for the server to store the product information in a database, means for the server to calculate an approximate purchase price based on the product information stored in the database, means for the server to recommend a purchase service specialized in a specific brand or category, means for the server to provide a method for disposing of priceless products, means for transmitting the calculation results and recommended information from the server to a user terminal, and means for the user terminal to display the received information to the user. This enables users to easily obtain purchase prices and purchase destination information for unwanted products and to receive optimal support tailored to their emotional state.

[1782] "User interface" refers to the screen layout and operating means that allow users to input product information, etc.

[1783] "Server" means a central control device that receives, stores, processes data and provides information to users.

[1784] A "database" is an information management system for storing product information, past purchase price data, purchaser information, market trends, etc.

[1785] A "purchase price calculation module" is software that includes an algorithm that calculates an approximate purchase price based on stored product information.

[1786] A "buyer recommendation module" is software that includes an algorithm that recommends appropriate buyers based on product information.

[1787] The "disposal recommendation module" is software that includes an algorithm that suggests appropriate disposal, recycling, or donation options for items that have no price tag.

[1788] An "emotion engine" is software that recognizes the user's emotional state and reflects it in the operation of the entire system.

[1789] "Emotional state" refers to the user's current psychological state, analyzed based on factors such as the user's operation speed, input frequency, and text mining of input content.

[1790] "Interface optimization" refers to changing the appearance and operation of an interface depending on the user's emotional state.

[1791] A "user terminal" is a device operated by a user, such as a smartphone.

[1792] "Product information" refers to data entered by the user, such as the product brand name, category, purchase date, product condition, and images.

[1793] The "calculation result" refers to the approximate purchase price calculated by the purchase price calculation module.

[1794] "Recommendation information" is information on buyers and disposal methods provided by the buyer recommendation module and disposal method recommendation module.

[1795] The system of the present invention allows users to easily obtain information on the purchase prices and buyers of unwanted items, and also provides optimal support according to the user's emotional state. The overall configuration of this system is as follows.

[1796] Overall system configuration

[1797] 1. User Interface (UI)

[1798] Users use a smartphone application to input product information, which has a function to scan barcodes and QR codes and automatically input the scanned product information.

[1799] 2. Emotion Engine

[1800] The system analyzes the user's emotional state based on their input speed and operation method. It uses a generative AI model for emotion analysis and determines the user's emotional state through text mining of the input content.

[1801] 3. Server

[1802] Product information storage: Product information sent by users is saved on the server and stored in a database. Product information includes brand name, category, purchase date, product condition, images, etc.

[1803] Calculation of purchase price: The purchase price calculation module on the server calculates the purchase price based on the product information stored in the database. The calculation is performed based on past purchase price data for similar products and market trends.

[1804] Buyer recommendation: The buy-back recommendation module on the server searches the database for buy-back services that specialize in specific brands or categories, and recommends the most suitable buy-back service to the user.

[1805] Providing disposal methods: For items that are not eligible for purchase, a disposal recommendation module on the server will suggest recycling methods or donation destinations to users.

[1806] 4. User Device

[1807] The system receives calculation results and recommendation information sent from the server and displays them to the user. The interface is also optimized according to the user's emotional state. For example, if the user is feeling anxious, the system will display more detailed explanations to reassure the user.

[1808] Hardware and software used

[1809] Hardware

[1810] Smartphone camera (for scanning product information)

[1811] Server (for data processing and storage)

[1812] software

[1813] OpenCV (for camera operation and image processing)

[1814] TensorFlow / Keras (for emotion recognition models)

[1815] Transformers (generative AI models for sentiment analysis)

[1816] Python standard library (for data processing and communication control)

[1817] Specific examples

[1818] The specific steps for a user to scan a brand-name bag they no longer use at home with the app and find out the purchase price are as follows:

[1819] 1. The user scans the bag's barcode with their smartphone camera.

[1820] 2. The scanned product information is sent to the server.

[1821] 3. The server calculates the purchase price and recommends the optimal buyer and disposal method.

[1822] 4. The emotion engine analyzes the user's emotional state based on their input.

[1823] 5. The interface is optimized according to the user's emotional state, and calculation results and recommendation information are displayed on the user's device.

[1824] Prompt Sentence Examples

[1825] "Please enter designer bag"

[1826] "Please enter product information: brand name, category, year of purchase, condition"

[1827] "Scan the product with your camera"

[1828] "How are you feeling right now? (free input)"

[1829] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1830] Step 1:

[1831] The user scans a product with the smartphone camera. The user launches the application and uses the camera function to read the product's barcode or QR code. The image data is taken as input, and the barcode information is analyzed using OpenCV. Product information (brand name, category, year of purchase, condition) is obtained as output.

[1832] Step 2:

[1833] The terminal transmits the acquired product information to the server. The terminal structures the product information and transmits it to the server using a secure communication protocol. The terminal receives product information as input and transmits it to the server as output.

[1834] Step 3:

[1835] The server receives the product information and stores it in a database. The server stores the received product information in a database and performs standard database operations to maintain data integrity. The server receives product information as input and stores it in a database as output.

[1836] Step 4:

[1837] The server's purchase price calculation module calculates the purchase price based on the information in the database. The server calculates the purchase price based on the saved product information, referring to past purchase price data for similar products and market trends. It receives product information and market data as input and calculates an approximate purchase price as output.

[1838] Step 5:

[1839] The server's buyback recommendation module searches for the most suitable buyback service. The server searches the database for buyback services that specialize in specific brands or categories and recommends the most suitable buyback service. It receives product information as input and creates a list of recommended buyback services as output.

[1840] Step 6:

[1841] The server's disposal recommendation module provides appropriate disposal methods. For items that are not eligible for purchase, the server suggests appropriate recycling methods or donation information to the user. It receives product information as input and creates a list of disposal methods as output.

[1842] Step 7:

[1843] The server's emotion engine analyzes the user's emotional state. It uses a generative AI model to analyze the emotional state based on data such as the user's free description and operation speed. It receives the user's operation data as input and obtains the user's emotional state as output.

[1844] Step 8:

[1845] The server optimizes the interface according to the emotional state and sends the calculation results and recommendation information to the terminal.The server takes the user's emotional state into consideration and optimizes the interface in simple or detailed form, then sends the calculation results and recommendation information to the terminal.The server receives the emotional state, calculation results, and recommendation information as input, and sends the optimized information to the user's terminal as output.

[1846] Step 9:

[1847] The terminal displays the received information to the user. The terminal displays the calculation results, recommendation information, and optimized interface sent from the server to the user. It receives information from the server as input and visualizes and presents it to the user as output.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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).

[1855] 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.

[1856] 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 o...

Claims

1. a means for a user to input product information; means for transmitting the product information to a server; a means for the server to store the product information in a database; A means for the server to calculate an approximate purchase price based on the product information stored in the database; A means for the server to recommend a buyback service specialized in a specific brand or category; A means for the server to provide a method for disposing of priceless items; means for transmitting the calculation results and recommendation information from the server to a user terminal; The system includes means for displaying the information received by said user terminal to a user.

2. 2. The system according to claim 1, wherein the server calculates an approximate purchase price based on past purchase price data of similar products and market trends.

3. 2. The system according to claim 1, wherein the server searches a database for buy-back services that specialize in a particular brand or category, and recommends the most suitable buy-back service.

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