System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting belongings information, a means for specifying a commodity on the basis of the belongings information, a means for acquiring a market value of the specified commodity, a means for monitoring fluctuation of the market value and notifying a user at an appropriate timing, and a means for supporting a procedure for disposing of the commodity.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] This invention relates to a system that supports the proper management and disposal of unwanted items. Many homes are filled with valuable unwanted items, but there are few ways to properly manage and efficiently dispose of them. As a result, many of these items end up being discarded as garbage, increasing the burden on the environment and resulting in economic losses. This invention aims to solve these issues by providing a system that utilizes IT technology to maximize the value of unwanted items and support efficient disposal at the appropriate time. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining the market value of the identified products, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, and a means for supporting procedures for disposing of the products. When the belongings information is in image format, the system also includes a means for analyzing the image to obtain detailed information about the identified products, and the market value is obtained based on data from multiple e-commerce platforms on the Internet. This maximizes the value of unwanted items and enables them to be efficiently disposed of at the appropriate time, thereby reducing environmental impact and economic loss.

[0006] "Belongings information" is information about items owned by the user, including an image of the item, its name, category, purchase date, condition, and so on.

[0007] "Item identification" is the process of identifying the details of the item in question based on the belongings information and identifying information such as its manufacturer, model, and specifications.

[0008] "Market value" refers to the price or value of an item when it is traded in the market, and is calculated based on the current supply and demand balance and past transaction data.

[0009] "Monitoring fluctuations" means monitoring fluctuations in market value over a period of time and tracking trends, such as rising or falling prices, in real time.

[0010] "Notification" refers to the means of communication by which the system notifies the user of important information, including in-app pop-up messages and push notifications.

[0011] "Supporting disposal procedures" means guiding users through specific methods for letting go of items and helping with processes such as selling, donating, recycling, and discarding.

[0012] "Image format" refers to a format in which information about an item is provided as a photograph or image file, which allows the appearance and characteristics of the item to be visually grasped.

[0013] "Analyzing an image" is the process of using image processing techniques to extract features about items in an image and use that information for identification or classification.

[0014] An "e-commerce platform" refers to an online marketplace or service that trades goods and services over the Internet, including mail order sites and auction sites.

[0015] "Data-driven" refers to using data collected from multiple sources to make specific analyses and decisions, which are used to assess market value and forecast trends. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system for supporting the appropriate management and disposal of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, and a means for supporting procedures for disposing of the products.

[0038] Specific explanation of program processing

[0039] 1. The user takes a photo of an object and registers it

[0040] Users use their smartphone camera to take a photo of the item they want to get rid of and upload it to the dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, condition, etc.).

[0041] 2. The device sends the image to the server

[0042] The terminal combines the images uploaded by the user and the basic information entered into a single data packet and transmits it to a server via the Internet.

[0043] 3. The server analyzes the image and identifies the product

[0044] The server analyzes the received image using an image analysis algorithm to extract the features of the object. Then, based on the extracted features, it identifies the product using a pre-trained machine learning model. For example, if a user registers an "old laptop," the server can analyze the image and identify it as a "Model X laptop from Manufacturer A."

[0045] 4. The server researches product value on the Internet

[0046] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the market value of "Manufacturer A's Model X Laptop" is collected from mail-order and auction sites, and the average transaction price is calculated.

[0047] 5. The server notifies you based on the research results.

[0048] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away the property, and notifies the user of the results. Notifications are sent as pop-ups on the app or push notifications. For example, the server might notify the user that "now is the time to sell, as the current market value is high."

[0049] 6. The server provides after-sales support on how to let go

[0050] The server suggests appropriate selling platforms and potential buyers. It also automatically generates product descriptions and descriptions for selling items and provides them to users. It also provides users with information on recycling and disposal methods. For example, the server automatically generates a product description for selling a "Model X laptop from Manufacturer A" on an online shopping site, and also provides information on high-price deals at second-hand shops.

[0051] Specific examples

[0052] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo and basic information to the server, which analyzes the image and identifies the item. The server then researches market value and notifies the user when a high price can be expected. Finally, the server helps the user sell the smartphone on the platform of their choice, generates a description, and provides potential buyers.

[0053] Through this system, users can easily maximize the value of their items and smoothly let go of them in the right way.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Users simply open their smartphone camera and take a photo of the item they want to declutter. After taking the photo, they open the dedicated Danshari Agent app and upload the photo to the app. They then fill in a form with basic information about the item, such as the category, purchase date, and current condition.

[0057] Step 2:

[0058] The terminal combines the images uploaded by the user and the basic information entered into a single data packet, which is then sent to a server via the Internet.

[0059] Step 3:

[0060] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0061] Step 4:

[0062] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0063] Step 5:

[0064] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. The server collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server obtains information such as "The average transaction price on mail-order site B over the past month was X yen."

[0065] Step 6:

[0066] Based on the data collected and analyzed by the server, an algorithm is used to determine the optimal time to sell, replace, or give away the property. The server generates a pop-up message or push notification in the app to notify the user of its results. For example, the server could notify the user that "now is the time to sell because the current market value is high."

[0067] Step 7:

[0068] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the item and provides them to the user. The server also provides information on recycling and disposal methods. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0069] This process allows users to maximize the value of their items without any hassle, and allows them to declutter efficiently and appropriately.

[0070] Example 1

[0071] 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."

[0072] In modern society, the number of items owned by individuals continues to increase, making the proper management and disposal of unwanted items an important issue. In particular, it is difficult for users to accurately grasp the market value of their belongings and to part with them at the optimal time. In this situation, there is a need for a method that reduces the burden on users and allows them to dispose of items efficiently.

[0073] 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.

[0074] In this invention, the server includes means for a user to input belongings information, means for transmitting the belongings information from a terminal to the server, means for the server to identify products based on the belongings information, means for obtaining market values ​​of the identified products from multiple e-commerce platforms on the Internet, means for monitoring fluctuations in the market values ​​and notifying the user at an appropriate time, and means for supporting procedures for disposing of the products and follow-up care, thereby enabling users to easily grasp the market values ​​of their belongings and let them go at the optimal time.

[0075] "User" refers to an individual who uses the system to manage and properly dispose of their belongings.

[0076] "Belonging information" is detailed information about an item that a user inputs into the system, including an image, category, purchase date, condition, and so on.

[0077] A "terminal" is a computer device that a user uses to input information about their belongings and send it to a server, and specifically refers to a smartphone, tablet, or the like.

[0078] "Server" refers to a central computer system that analyzes the received inventory information and provides functions such as identifying the item, obtaining its market value, and notifying the user.

[0079] "Image analysis algorithm" refers to a calculation method for extracting features of an item from an image received by the server and identifying the product based on that.

[0080] "Generative AI model" refers to an artificial intelligence model used to analyze detailed information about items identified after image analysis and determine the optimal time and method for users to let go of the item.

[0081] An "e-commerce platform" refers to a website or service where goods are bought and sold over the Internet, and is primarily the subject of market value data collection.

[0082] "Data packet" refers to a unit of data packed in a certain format used to transmit information over a network.

[0083] "Market value" refers to the prevailing price at which the identified item is traded in the current market, and is calculated based on transaction data obtained from e-commerce platforms.

[0084] "Notification" refers to the action taken by the server to inform the user of important information such as market value and the best time to sell, and can take the form of a pop-up display or push notification.

[0085] "Procedures and after-sales support" refers to a series of tasks and suggestions to support users in the process of letting go of their items, including automatically generating a sales description and suggesting suitable buyers.

[0086] This invention is a system that supports the proper management and disposal of unwanted items. This system is mainly composed of three elements: the user, the terminal, and the server.

[0087] First, the user takes a photo of the item they want to get rid of using their smartphone, tablet, or other device. Next, the user enters the photo along with basic information such as the category, purchase date, and current condition into a dedicated app, and sends it to the server via the app. The data sent is encrypted using the SSL / TLS protocol and delivered securely to the server.

[0088] Data sent from the device arrives at the server in the form of a data packet containing an image and basic information. The server first analyzes the received image. This analysis uses image analysis algorithms such as TENSORFLOW (registered trademark) and PyTorch. This extracts the object's features (shape, color, manufacturer logo, etc.), and a generative AI model identifies the product based on these features. For example, if a user uploads an image of an old laptop, the server analyzes the image and identifies it as a "Model X laptop from Manufacturer A."

[0089] Next, the server crawls major e-commerce platforms (e.g., Amazon, eBay, Yahoo! Auctions, etc.) to obtain the market value of the identified products. This crawling is performed using web scraping tools such as BeautifulSoup and Scrapy. The server collects data from each platform, such as the current market value, price fluctuation trends, and past transaction history, and analyzes this data to calculate the average transaction price of the product.

[0090] The server then uses the collected data and the generated AI model to determine the best time for the user to sell or replace the item. The results are communicated to the user via a pop-up notification or push notification on the app. For example, the server may notify the user, "The current market value is high, so now is the time to sell."

[0091] Finally, the server also has functions to support the process of letting go of items, such as suggesting suitable selling platforms and potential buyers, automatically generating product descriptions and descriptions for items, and providing information on recycling and disposal methods. This allows users to smoothly let go of their items and maximize their value.

[0092] Specific examples

[0093] When a user wants to sell their old smartphone, they first take a photo of it, upload it to a dedicated app, and enter basic information. The device then sends this data to a server, which analyzes the image and identifies the item. The server then researches the market value from e-commerce platforms and notifies the user of the best time to sell. Finally, the server automatically generates a sales description and suggests suitable selling platforms and potential buyers.

[0094] Prompt Sentence Examples

[0095] Here is an example of a prompt to input to a generative AI model:

[0096] "Research the market value of your old smartphone and, based on that, design a system that will tell you the best time to sell it."

[0097] Through this system, users can easily understand the value of their belongings and decide when to let them go.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user inputs information about their belongings. They use the device's camera to take a photo of the item they want to get rid of and upload it to a dedicated app. They also input basic information such as the category (e.g., electronic device), purchase date, and condition (e.g., used). The input data is saved in the app as image data and text data.

[0101] Input: Image data, basic information (category, purchase date, condition)

[0102] Output: Inventory information stored in the app

[0103] Step 2:

[0104] The device sends image data and basic information to the server. The device then assembles the information entered by the user into a single data packet and sends it over the Internet to the server. The SSL / TLS protocol is used to ensure data integrity and security.

[0105] Input: Possession information (image data, basic information)

[0106] Output: Data packets sent to the server

[0107] Step 3:

[0108] The server analyzes the image and identifies the product. The server opens the received image data and analyzes the image using image analysis algorithms such as TensorFlow or PyTorch. The object's features (e.g., shape, color, logo) are extracted, and based on those features, a generative AI model is used to identify the product.

[0109] Input: Image data

[0110] Output: Identified product (e.g., laptop model Y from manufacturer X)

[0111] Step 4:

[0112] The server researches the market value of identified products on the Internet. The server uses web scraping tools such as BeautifulSoup and Scrapy to collect data from major e-commerce platforms. This allows the server to obtain the current market value, price fluctuation trends, past transaction history, etc. The server analyzes this data and calculates the average transaction price of the product.

[0113] Input: Identified product

[0114] Output: Market value data (current market value, price fluctuation trend, average transaction price)

[0115] Step 5:

[0116] The server notifies the user based on the research results. Based on the collected and analyzed market value data, the server uses a generative AI model to determine the optimal time to sell or replace the property. The results are communicated to the user via a pop-up notification or push notification on the app.

[0117] Input: Market value data, judgment results of the generated AI model

[0118] Output: Notification message (e.g., current market value is high, now is the time to sell)

[0119] Step 6:

[0120] The server provides users with after-sales support, suggests suitable selling platforms and potential buyers, automatically generates product descriptions and introductory text for selling items, and provides users with information on recycling and disposal methods.

[0121] Input: Product information that the user is selling, sales platform information

[0122] Output: Proposal details (selling platform, potential buyers, introduction, recycling information)

[0123] (Application example 1)

[0124] 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."

[0125] In modern society, people are required to properly manage unwanted items and let them go at the optimal time. However, many users find it difficult to accurately determine the market value of their belongings, making it difficult to find the right way to let go. Other issues include the time-consuming process of creating a sales description and selecting the optimal sales platform.

[0126] 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.

[0127] In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for automatically generating a sales description using a generative AI model, and means for suggesting selling the sales description on an optimal platform. This allows the user to accurately grasp the market value of their belongings and sell them at the optimal time, and also makes it easy to create a sales description and select a sales platform when selling.

[0128] "Belongings information" is detailed data about products and items that a user possesses, and includes image and text data.

[0129] "Means for identifying products" refers to a technical method for analyzing the entered belongings information and identifying specific product names and model names.

[0130] "Means for obtaining market value" refers to a technical method for obtaining the current market price or transaction price of a specified commodity based on data on the Internet.

[0131] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to a technological method for tracking fluctuations in market value in real time and informing users of the optimal timing for selling based on that information.

[0132] "Means to support the procedures for disposing of products" refers to technological methods that suggest ways for users to dispose of unwanted items, such as selling, transferring, or recycling, and facilitate the procedures.

[0133] A "generative AI model" is a machine learning model that uses artificial intelligence to automatically generate text, and has the ability to create sentences based on large amounts of data.

[0134] "Means for automatically generating sales descriptions" means a technical method for automatically generating sales descriptions highlighting the features and benefits of a product identified using a generative AI model.

[0135] The "means for suggesting the optimal platform for selling" refers to a technological method for recommending to a user the e-commerce platform on which the product can be most effectively sold based on the acquired market value data and product characteristics.

[0136] The present invention relates to a system for supporting the proper management and parting of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, a means for supporting procedures for disposing of the products, a means for automatically generating sales descriptions using a generative AI model, and a means for suggesting selling the sales descriptions on an optimal platform.

[0137] Specific explanation of program processing

[0138] 1. Enter your belongings information

[0139] Users use their smartphones to take a photo of the item they want to sell and upload it to the app, along with basic information such as the category, purchase date, and condition.

[0140] 2. Sending images and information

[0141] The device (smartphone) collects the uploaded images and the basic information entered into a data packet and sends it to a server over the Internet using HTTPS, a protocol that ensures secure data transmission.

[0142] 3. Image analysis for product identification

[0143] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to analyze the received images. Specifically, it identifies the product using an image classification model such as ResNet. In this step, it extracts product features from the image and identifies the specific product name and model name.

[0144] 4. Market Value Research

[0145] The server scrapes data from major e-commerce platforms (e.g., Amazon, eBay, major auction sites) to obtain market values ​​associated with identified products. For this purpose, scraping tools such as Beautiful Soup and Scrapy are used. The data is then analyzed to determine current market prices and price fluctuation trends.

[0146] 5. Notification of optimal timing

[0147] The server determines the optimal time to sell based on the acquired market value data and notifies the user. This notification is sent in real time via push notifications using Firebase Cloud Messaging.

[0148] 6. Auto-generated sales pitch

[0149] The server uses a generative AI model (e.g., GPT-3 (registered trademark)) to automatically generate a sales description that reflects the identified product's features and benefits. This description is an important factor in helping users sell their products at a higher price.

[0150] 7. Proposal of the best platform

[0151] The server proposes the most suitable sales platform for the user based on the acquired market value data and automatically generated product descriptions, allowing the user to easily find the best place to sell their products.

[0152] Specific examples

[0153] For example, consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone, uploads it to the app, and enters basic information such as when it was purchased and its condition. The device then sends this information to a server, which performs image analysis to identify the item. The server then researches market value from multiple online e-commerce platforms and notifies the user of the best time to sell. Furthermore, a generative AI model is used to automatically generate a sales description and suggests how to sell the item on the most suitable platform.

[0154] Examples of prompt statements

[0155] Examples of prompts include:

[0156] "Please generate a testimonial for a smartphone in good condition that you purchased in March 2021. Also, please suggest an effective way to sell it."

[0157] This allows users to easily maximize the value of their belongings and smoothly part with them in the right way.

[0158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0159] Step 1:

[0160] The user uses their smartphone to take a photo of the item they want to get rid of and uploads it to the application. The input includes the product image and basic information (category, purchase date, condition). Based on this information, a data packet is generated to proceed to the next step.

[0161] Step 2:

[0162] The device (smartphone) combines the uploaded image and the entered basic information into a single data packet and sends it to the server via the Internet. The input is the product image and basic information data, and the output is a data packet sent to the server. This process uses the HTTPS protocol to ensure secure data transfer.

[0163] Step 3:

[0164] The server analyzes the received data packets and identifies the products using image analysis algorithms, such as TensorFlow or PyTorch, to extract features from the product images. The input is the image data and basic information, and the output is detailed information about the identified products (make, model, and other relevant information).

[0165] Step 4:

[0166] The server scrapes major e-commerce platforms to obtain the market value of the identified products. Tools such as Beautiful Soup and Scrapy are used to obtain price information published on the Internet. The input is product identification information, and the output is market value data (current market price, price fluctuation trend).

[0167] Step 5:

[0168] The server determines the optimal time to sell based on the acquired market value data. Here, an algorithm is used to analyze price fluctuation trends. The input is market value data, and the output is information on the optimal time to sell, which is notified to the user. This information is pushed in real time using Firebase Cloud Messaging.

[0169] Step 6:

[0170] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the features and benefits of the identified product. The input is product-specific information and basic information, and the output is the generated description. This description is then sent to the user's smartphone application.

[0171] Step 7:

[0172] The server recommends the most suitable sales platform for the user based on the acquired market value data and the automatically generated introduction. The input is the market value data and the generated introduction, and the output is information on the recommended sales platform. Based on this, the user can take specific steps to sell the product.

[0173] 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.

[0174] The present invention relates to a system that supports the appropriate management and disposal of unwanted items. The system includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining the market value of the identified products, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, and a means for supporting procedures for disposing of the products. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized notifications and advice.

[0175] Specific explanation of program processing

[0176] 1. The user takes a photo of an object and registers it

[0177] Users use their smartphone (device) camera to take a photo of the item they want to get rid of and upload it to a dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, current condition, etc.). At this time, the emotion engine also works, analyzing the user's facial expressions and voice to recognize their current emotion.

[0178] 2. The device sends the image to the server

[0179] The device combines the image uploaded by the user, the basic information entered, and the recognized emotion information into a single data packet and transmits it to a server via the Internet.

[0180] 3. The server analyzes the image and identifies the product

[0181] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0182] 4. The server retrieves detailed information about the identified product

[0183] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0184] 5. The server researches product value on the Internet

[0185] The server crawls major e-commerce platforms to obtain market values ​​related to the identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the server collects the market value of "Manufacturer A's Model X Laptop" from mail-order and auction sites and calculates the average transaction price.

[0186] 6. The server notifies you based on the research results.

[0187] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away. Furthermore, the server adjusts the content and timing of notifications based on the user's emotions as recognized by the emotion engine. For example, if the server notifies the user that "the current market value is high, so now is the time to sell," but the emotion engine recognizes that the user appears busy, the server will delay the notification.

[0188] 7. The server provides follow-up on how to let go

[0189] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the product and provides them to the user. It also provides information on recycling and disposal methods to the user. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0190] Specific examples

[0191] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo, basic information, and the user's emotional state to the server. The server analyzes the image to identify the item and research its market value. It takes the user's emotional state into consideration when notifying them of when a high price can be expected. Finally, the server supports the sale on the platform selected by the user, generates a description, and provides potential buyers.

[0192] Through this system, users can maximize the value of their items without much effort and declutter efficiently and appropriately.The incorporation of an emotion engine enables flexible responses according to the user's situation, increasing user satisfaction.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] The user activates the smartphone camera and takes a photo of the item they want to declutter. The emotion engine then captures the user's face with the camera and recognizes their emotion from their facial expressions and voice. After taking the photo, the user opens the dedicated Danshari Agent app, uploads the photo, and also enters basic information such as the item's category, purchase date, and condition.

[0196] Step 2:

[0197] The terminal combines the image uploaded by the user, the input basic information, and the user's emotional information (e.g., relaxed, stressed, harmful, etc.) recognized by the emotion engine into a single data packet, which is then sent to a server via the Internet.

[0198] Step 3:

[0199] The server analyzes the received image using an image analysis algorithm. It extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server then identifies the product based on a machine learning model trained using the feature vector. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0200] Step 4:

[0201] The server retrieves detailed information about the identified item (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0202] Step 5:

[0203] The server crawls major e-commerce platforms to obtain the market value of a specified product. It collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server might determine that "the average transaction price on mail-order site B over the past month was X yen."

[0204] Step 6:

[0205] Based on the data collected and analyzed by the server, an algorithm determines the optimal time to sell, replace, or give away the vehicle. At the same time, it also takes into account the user's emotional state and adjusts the content and timing of notifications. For example, if the server determines that "the current market value is high, so now is the time to sell," it will notify the user immediately if the user is relaxed, or at an appropriate time if the user is feeling stressed.

[0206] Step 7:

[0207] The server supports the process of disposing of the product. This includes suggesting suitable selling platforms, suggesting potential buyers, and automatically generating a sales description and product description. The server also provides users with information on recycling and disposal methods. For example, "Automatically generate a sales description for a Model X laptop from manufacturer A on online shopping site C, and also provide information on high-price offers at second-hand shops."

[0208] This process allows users to get maximum benefit with minimum effort, and the emotion engine delivers personalized responses.

[0209] Example 2

[0210] 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."

[0211] In today's world, managing and disposing of personal possessions is difficult, especially when it comes to finding the right way to dispose of items that are no longer in use. It's also difficult to understand the market value of items and dispose of them at the optimal time. Furthermore, there's a problem with low user satisfaction, as notifications and advice aren't provided that take the user's feelings into consideration.

[0212] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting belongings information, a means for identifying an object based on the belongings information, a means for acquiring the market value of the identified object, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, a means for supporting procedures for disposing of the object, and a means for recognizing the user's emotions and adjusting the content and timing of the notification. This allows the user to easily manage and dispose of items, and dispose of them at the optimal time while maximizing the value of the items. Furthermore, personalized services that take emotions into consideration are provided, which can improve user satisfaction.

[0213] "Belongings information" is information about items owned by the user, and specifically includes detailed information such as category, purchase date, and current condition.

[0214] The "object" refers to an item that the user wishes to dispose of or manage, and is identified based on a photographed image and input information.

[0215] "Market value" refers to the value of an item when it is currently traded in the market, and is determined based on data obtained from e-commerce platforms, etc.

[0216] An "emotion engine" refers to technology or software that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0217] "Notification" refers to information sent from the server to the user, and includes information regarding fluctuations in market value and optimal disposal timing.

[0218] "Procedures" refers to the specific methods and procedures for disposing of the object, including support for actions such as selling, transferring, or discarding.

[0219] An "algorithm" refers to a set of calculation procedures or rules for solving a specific problem, and is used here to analyze market value and adjust notification timing.

[0220] This invention relates to a system that supports the proper management and disposal of unwanted items. This system works in conjunction with devices such as smartphones and tablets, and primarily assists in the input of belongings information, product identification, acquisition and monitoring of market value, timely notifications, and product disposal procedures. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized notifications and advice.

[0221] Hardware and software used

[0222] This system uses the following hardware and software:

[0223] Devices: smartphones, tablets

[0224] Server: Cloud server, database server

[0225] Software: Danshari agent app, image analysis algorithm, emotion engine, e-commerce platform crawling tool, generative AI model

[0226] Specific data processing and calculation

[0227] The user takes a photo of an object and registers the information.

[0228] Users use their device's camera to take a photo of the item they want to declutter and upload it to the dedicated Danshari Agent app. In addition to the photo, users enter basic information such as the category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice in real time to recognize their current emotional state.

[0229] The device sends the image and information to the server.

[0230] The device then combines the photo, basic information, and emotion information into a single data packet and transmits it to a server over the Internet using a secure communication protocol (e.g., HTTPS).

[0231] The server analyzes the image and identifies the product

[0232] The server analyzes the received image using an image analysis algorithm based on computer vision technology. The server extracts features such as shape, color, and brand logos from the image and generates a feature vector. This feature vector is then used to identify the relevant product based on a pre-trained machine learning model.

[0233] The server retrieves detailed information about the identified product.

[0234] The server retrieves detailed information about the identified product (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0235] The server researches product values ​​on the Internet

[0236] The server crawls major e-commerce platforms (e.g., Amazon, eBay) to obtain the market value of the identified product. It then collects and analyzes data such as market value, price fluctuation trends, and past transaction history to calculate the current market value.

[0237] The server will notify you based on the research results.

[0238] The server uses an algorithm to determine the optimal time to sell, replace, or give away. It also adjusts the content and timing of notifications based on the user's emotions as determined by the emotion engine. For example, the server may notify users that "the current market value is high, so now is the time to sell," but if the emotion engine determines that the user is busy, it will delay the notification.

[0239] The server will provide follow-up on how to let go

[0240] The server suggests suitable selling platforms and potential buyers, and also uses a generative AI model to automatically generate sales descriptions and product descriptions, as well as provide users with information on recycling and disposal methods.

[0241] Specific examples

[0242] When a user has an old smartphone they want to get rid of, they take a photo of it and upload it to the app. The device then sends the photo, basic information, and the user's emotional state to a server. The server analyzes the image to identify the item and researches its market value, notifying users when a high price can be expected. The server then supports the sale on the user's platform of choice, generates a sales description, and provides potential buyers, allowing users to efficiently dispose of their items.

[0243] Prompt Sentence Examples

[0244] "Please describe your system for helping people dispose of their old smartphones. Explain in detail how the system retrieves information from users' smartphones, uses that information to determine market value, and recommends the best time and method for selling."

[0245] This allows users to maximize the value of their items without much effort and declutter efficiently and appropriately.The emotion engine enables flexible responses tailored to the user's situation, which is expected to increase user satisfaction.

[0246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0247] Step 1:

[0248] The user takes a photo of the item they want to declutter and uploads it using the device app. As input, the user provides basic information such as the image file, category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice to generate current emotion information. As output, the device combines the photo, basic information, and emotion information into a single data packet.

[0249] Step 2:

[0250] The device sends data to the server. As input, the device receives a data packet that it has assembled. The device transmits the data packet to the server over the Internet using a secure communication protocol (e.g., HTTPS). The output of this transmission process is the data received by the server.

[0251] Step 3:

[0252] The server extracts image data from the received data packets and performs image analysis. As input, it uses the image file and basic information sent to the server. Using computer vision techniques, the server extracts features such as shape, color, and brand logos in the image and generates a feature vector. As output, it obtains the feature vector and analyzed data.

[0253] Step 4:

[0254] The server identifies the product using the feature vector. The feature vector obtained by image analysis is used as input. The server inputs the feature vector into a machine learning model to identify the product. The output is detailed information about the identified product (manufacturer, model, specifications, etc.).

[0255] Step 5:

[0256] The server retrieves the identified product details from the database. Using the identified product details as input, the server executes a database query to retrieve the relevant details. The output is the product details retrieved from the database.

[0257] Step 6:

[0258] The server collects market value information from the Internet. It uses detailed information about the identified product as input. The server crawls major e-commerce platforms to collect data such as the product's price, transaction history, and price fluctuation trends. The output is the collected market value data.

[0259] Step 7:

[0260] The server analyzes the market value data and notifies the user based on the results. The collected market value data and sentiment information are used as input. The server uses an algorithm to determine the optimal timing for selling and adjusts the content and timing of the notification taking into account the user's sentiment information. The output is the adjusted content and timing of the notification.

[0261] Step 8:

[0262] The server provides the user with support on how to sell the item. As input, it uses the previously identified item details and market value data. The server suggests suitable selling platforms and potential buyers, and uses a generative AI model to automatically generate a listing and description for the item. It also provides information on recycling and disposal methods. The output is an automatically generated listing, a suggested selling platform, and advice on how to dispose of the item.

[0263] (Application example 2)

[0264] 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."

[0265] In modern society, many people find it difficult to properly manage and part with unnecessary items. This problem stems from a lack of information to understand the market value of items and decide when to part with them. Furthermore, the lack of personalized advice based on the user's emotional state makes it difficult to make decisions about decluttering. There is a growing need for a system that provides efficient and appropriate support to address these challenges.

[0266] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for recognizing the user's emotions and providing personalized notifications and advice, means for registering and managing products using a smartphone, means for identifying products using an image analysis algorithm, means for acquiring market values ​​from multiple e-commerce platforms on the Internet, and means for providing advice tailored to the user's situation using an emotion engine. This not only enables users to easily understand the market value of items and decide when to part with them, but also provides personalized advice tailored to the user's emotional state, thereby supporting the user's decision-making regarding decluttering.

[0267] - "Belongings Information" refers to detailed information about the items owned by the user, including the type of item, when it was purchased, its current condition, etc.

[0268] "Means for identifying the product" refers to a technical method for identifying the type of product an item is based on the entered belongings information.

[0269] "Means for obtaining market value" refers to a method for collecting the current market price or value of a specified commodity from data on the Internet.

[0270] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to technology that tracks trends in commodity market values ​​in real time and notifies users of the best time to sell or dispose of them.

[0271] "Means to support procedures for disposing of goods" refers to a system that provides users with information on how to choose a platform for selling goods, recycling methods, disposal procedures, etc.

[0272] "Means for recognizing a user's emotions and providing personalized notifications and advice" refers to technology that analyzes emotions from a user's facial expressions and voice, and uses that information to provide individually appropriate notifications and advice.

[0273] "Means for registering and managing products using a smartphone" refers to a method for entering and updating information about items owned by a user using a smartphone camera or application.

[0274] "Means for identifying products using image analysis algorithms" refers to technology that analyzes input image data and identifies the type of product the target item is based on the results.

[0275] "Means for obtaining market value from multiple e-commerce platforms on the Internet" refers to a method of collecting price information from multiple online marketplaces and calculating the market value of a product based on that data.

[0276] "Means for providing advice tailored to the user's situation using an emotion engine" refers to technology for providing advice with optimal timing and content based on the user's emotional state.

[0277] The present invention provides a system for supporting users in properly managing and letting go of their belongings, and is realized in the form of a smartphone application. The system includes the following means.

[0278] System Configuration

[0279] 1. How to enter your belongings:

[0280] Users take a photo of an item using their smartphone camera and upload it to the app, where they can enter basic information about the item (category, purchase date, current condition, etc.).

[0281] 2. Means of identifying the product:

[0282] The server uses image analysis algorithms to identify the item from the uploaded image, using computer vision techniques.

[0283] 3. Means of obtaining market value:

[0284] The server obtains the market value of the item from multiple e-commerce platforms on the Internet, and collects and analyzes market value data, price fluctuation trends, past transaction history, etc. related to the identified item.

[0285] 4. Means of monitoring market value fluctuations and notifying users in a timely manner:

[0286] The server uses an algorithm to determine the optimal time to sell based on the collected market value data and notifies the user. The content and timing of the notification also take into account the user's emotional state as recognized by an emotion engine.

[0287] 5. Supporting procedures for disposing of goods:

[0288] The server automatically generates and provides users with suitable selling platforms, potential buyers, and product descriptions for the items they are selling. It also provides information on recycling and disposal methods.

[0289] 6. How to recognize user emotions and provide personalized notifications and advice:

[0290] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and adjusts the content and timing of notifications based on the results.

[0291] Hardware and software used

[0292] Hardware:

[0293] Smartphone (camera, microphone, internet connection)

[0294] software:

[0295] Smartphone applications (e.g., Danshari Agent app)

[0296] Image analysis algorithms (e.g. OpenCV)

[0297] Machine learning models (e.g., scikit-learn)

[0298] Emotion Recognition Engine

[0299] Libraries for using data retrieval APIs (e.g., requests)

[0300] Specific examples

[0301] For example, if a user wants to get rid of an old smartphone, they can use the system as follows:

[0302] 1. The user takes a photo of their old smartphone and uploads it to the app. At the same time, they enter basic information about the item (category, purchase date, current condition, etc.). The prompt is: "I'd like to sell my old smartphone. I'll send you a photo so you can identify the item and tell me its market value. Also, please advise me on whether I should sell it now."

[0303] 2. The app sends the photo and basic information to a server, which uses image analysis algorithms to identify the item.

[0304] 3. The server obtains the market value of the identified item from multiple e-commerce platforms on the Internet and notifies the user of the optimal time to sell.

[0305] 4. An emotion engine analyzes the user's emotional state and provides personalized advice.

[0306] 5. Finally, we provide users with comprehensive support, including selecting a selling platform, automatically generating a sales description, and disposal methods such as recycling information.

[0307] In this way, the present invention allows users to maximize the value of their items efficiently and appropriately, making it easier for them to make decisions about decluttering.

[0308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0309] Step 1:

[0310] Users take a photo of the item they want to get rid of with their smartphone and upload it to the app. They then enter basic information about the item, such as the category, purchase date, and current condition. The input data consists of a photo image file and basic text data.

[0311] Step 2:

[0312] The device combines the uploaded photo and basic information into a single data packet and sends it to the server. The input data is the image file and the basic information text data, and the output data is the data packet sent to the server.

[0313] Step 3:

[0314] The server analyzes the received images using an image analysis algorithm to identify the item. This process uses computer vision technology (e.g., OpenCV). The input data is the image file, and the output data is detailed information about the identified item (e.g., manufacturer, model, specifications, etc.).

[0315] Step 4:

[0316] The server retrieves detailed information of the identified item from the database and links it to the belongings information. The input data is the feature vector of the item, and the output data is detailed information of the item.

[0317] Step 5:

[0318] The server obtains market values ​​from multiple e-commerce platforms on the Internet. This process often uses web crawling technology. The input data is detailed information about the item, and the output data is market value data (e.g., current market price, price fluctuation trends, past transaction history, etc.).

[0319] Step 6:

[0320] The server analyzes the collected market value data and determines the optimal time to sell, replace, or give away the property based on an algorithm. The input data is the market value data, and the output data is the result of the decision on when to sell.

[0321] Step 7:

[0322] The server recognizes the user's emotions using an emotion engine and adjusts the content and timing of notifications. The input data is the user's facial expressions and voice data, and the output data is the result of emotion analysis.

[0323] Step 8:

[0324] The server takes into account the sentiment analysis results and sends appropriate notifications to the user. The notification content includes messages such as "Now is the time to sell because the current market value is high." The input data are the sentiment analysis results and market value data, and the output data is the notification message to the user.

[0325] Step 9:

[0326] The server provides after-sales support for disposing of items. Specifically, it selects a sales platform, automatically generates a description, and provides information on recycling and disposal methods. The input data is detailed information about the item and its market value, and the output data is after-sales support information for the user.

[0327] Through the above steps, the present invention can provide a system that enables users to maximize the value of items efficiently and appropriately and supports decision-making regarding decluttering.

[0328] 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.

[0329] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0330] 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.

[0331] [Second embodiment]

[0332] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0333] 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.

[0334] 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).

[0335] 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.

[0336] 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.

[0337] 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).

[0338] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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."

[0344] The present invention relates to a system for supporting the appropriate management and disposal of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, and a means for supporting procedures for disposing of the products.

[0345] Specific explanation of program processing

[0346] 1. The user takes a photo of an object and registers it

[0347] Users use their smartphone camera to take a photo of the item they want to get rid of and upload it to the dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, condition, etc.).

[0348] 2. The device sends the image to the server

[0349] The terminal combines the images uploaded by the user and the basic information entered into a single data packet and transmits it to a server via the Internet.

[0350] 3. The server analyzes the image and identifies the product

[0351] The server analyzes the received image using an image analysis algorithm to extract the features of the object. Then, based on the extracted features, it identifies the product using a pre-trained machine learning model. For example, if a user registers an "old laptop," the server can analyze the image and identify it as a "Model X laptop from Manufacturer A."

[0352] 4. The server researches product value on the Internet

[0353] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the market value of "Manufacturer A's Model X Laptop" is collected from mail-order and auction sites, and the average transaction price is calculated.

[0354] 5. The server notifies you based on the research results.

[0355] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away the property, and notifies the user of the results. Notifications are sent as pop-ups on the app or push notifications. For example, the server might notify the user that "now is the time to sell, as the current market value is high."

[0356] 6. The server provides after-sales support on how to let go

[0357] The server suggests appropriate selling platforms and potential buyers. It also automatically generates product descriptions and descriptions for selling items and provides them to users. It also provides users with information on recycling and disposal methods. For example, the server automatically generates a product description for selling a "Model X laptop from Manufacturer A" on an online shopping site, and also provides information on high-price deals at second-hand shops.

[0358] Specific examples

[0359] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo and basic information to the server, which analyzes the image and identifies the item. The server then researches market value and notifies the user when a high price can be expected. Finally, the server helps the user sell the smartphone on the platform of their choice, generates a description, and provides potential buyers.

[0360] Through this system, users can easily maximize the value of their items and smoothly let go of them in the right way.

[0361] The processing flow will be explained below.

[0362] Step 1:

[0363] Users simply open their smartphone camera and take a photo of the item they want to declutter. After taking the photo, they open the dedicated Danshari Agent app and upload the photo to the app. They then fill in a form with basic information about the item, such as the category, purchase date, and current condition.

[0364] Step 2:

[0365] The terminal combines the images uploaded by the user and the basic information entered into a single data packet, which is then sent to a server via the Internet.

[0366] Step 3:

[0367] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0368] Step 4:

[0369] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0370] Step 5:

[0371] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. The server collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server obtains information such as "The average transaction price on mail-order site B over the past month was X yen."

[0372] Step 6:

[0373] Based on the data collected and analyzed by the server, an algorithm is used to determine the optimal time to sell, replace, or give away the property. The server generates a pop-up message or push notification in the app to notify the user of its results. For example, the server could notify the user that "now is the time to sell because the current market value is high."

[0374] Step 7:

[0375] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the item and provides them to the user. The server also provides information on recycling and disposal methods. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0376] This process allows users to maximize the value of their items without any hassle, and allows them to declutter efficiently and appropriately.

[0377] Example 1

[0378] 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."

[0379] In modern society, the number of items owned by individuals continues to increase, making the proper management and disposal of unwanted items an important issue. In particular, it is difficult for users to accurately grasp the market value of their belongings and to part with them at the optimal time. In this situation, there is a need for a method that reduces the burden on users and allows them to dispose of items efficiently.

[0380] 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.

[0381] In this invention, the server includes means for a user to input belongings information, means for transmitting the belongings information from a terminal to the server, means for the server to identify products based on the belongings information, means for obtaining market values ​​of the identified products from multiple e-commerce platforms on the Internet, means for monitoring fluctuations in the market values ​​and notifying the user at an appropriate time, and means for supporting procedures for disposing of the products and follow-up care, thereby enabling users to easily grasp the market values ​​of their belongings and let them go at the optimal time.

[0382] "User" refers to an individual who uses the system to manage and properly dispose of their belongings.

[0383] "Belonging information" is detailed information about an item that a user inputs into the system, including an image, category, purchase date, condition, and so on.

[0384] A "terminal" is a computer device that a user uses to input information about their belongings and send it to a server, and specifically refers to a smartphone, tablet, or the like.

[0385] "Server" refers to a central computer system that analyzes the received inventory information and provides functions such as identifying the item, obtaining its market value, and notifying the user.

[0386] "Image analysis algorithm" refers to a calculation method for extracting features of an item from an image received by the server and identifying the product based on that.

[0387] "Generative AI model" refers to an artificial intelligence model used to analyze detailed information about items identified after image analysis and determine the optimal time and method for users to let go of the item.

[0388] An "e-commerce platform" refers to a website or service where goods are bought and sold over the Internet, and is primarily the subject of market value data collection.

[0389] "Data packet" refers to a unit of data packed in a certain format used to transmit information over a network.

[0390] "Market value" refers to the prevailing price at which the identified item is traded in the current market, and is calculated based on transaction data obtained from e-commerce platforms.

[0391] "Notification" refers to the action taken by the server to inform the user of important information such as market value and the best time to sell, and can take the form of a pop-up display or push notification.

[0392] "Procedures and after-sales support" refers to a series of tasks and suggestions to support users in the process of letting go of their items, including automatically generating a sales description and suggesting suitable buyers.

[0393] This invention is a system that supports the proper management and disposal of unwanted items. This system is mainly composed of three elements: the user, the terminal, and the server.

[0394] First, the user takes a photo of the item they want to get rid of using their smartphone, tablet, or other device. Next, the user enters the photo along with basic information such as the category, purchase date, and current condition into a dedicated app, and sends it to the server via the app. The data sent is encrypted using the SSL / TLS protocol and delivered securely to the server.

[0395] Data sent from the device arrives at the server in the form of a data packet containing an image and basic information. The server first analyzes the received image using image analysis algorithms such as TensorFlow and PyTorch. This extracts the object's features (shape, color, manufacturer logo, etc.), and a generative AI model identifies the product based on these features. For example, if a user uploads an image of an old laptop, the server analyzes the image and identifies it as "Model X laptop from Manufacturer A."

[0396] Next, the server crawls major e-commerce platforms (e.g., Amazon, eBay, Yahoo! Auctions, etc.) to obtain the market value of the identified products. This crawling is performed using web scraping tools such as BeautifulSoup and Scrapy. The server collects data from each platform, such as the current market value, price fluctuation trends, and past transaction history, and analyzes this data to calculate the average transaction price of the product.

[0397] The server then uses the collected data and the generated AI model to determine the best time for the user to sell or replace the item. The results are communicated to the user via a pop-up notification or push notification on the app. For example, the server may notify the user, "The current market value is high, so now is the time to sell."

[0398] Finally, the server also has functions to support the process of letting go of items, such as suggesting suitable selling platforms and potential buyers, automatically generating product descriptions and descriptions for items, and providing information on recycling and disposal methods. This allows users to smoothly let go of their items and maximize their value.

[0399] Specific examples

[0400] When a user wants to sell their old smartphone, they first take a photo of it, upload it to a dedicated app, and enter basic information. The device then sends this data to a server, which analyzes the image and identifies the item. The server then researches the market value from e-commerce platforms and notifies the user of the best time to sell. Finally, the server automatically generates a sales description and suggests suitable selling platforms and potential buyers.

[0401] Prompt Sentence Examples

[0402] Here is an example of a prompt to input to a generative AI model:

[0403] "Research the market value of your old smartphone and, based on that, design a system that will tell you the best time to sell it."

[0404] Through this system, users can easily understand the value of their belongings and decide when to let them go.

[0405] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0406] Step 1:

[0407] The user inputs information about their belongings. They use the device's camera to take a photo of the item they want to get rid of and upload it to a dedicated app. They also input basic information such as the category (e.g., electronic device), purchase date, and condition (e.g., used). The input data is saved in the app as image data and text data.

[0408] Input: Image data, basic information (category, purchase date, condition)

[0409] Output: Inventory information stored in the app

[0410] Step 2:

[0411] The device sends image data and basic information to the server. The device then assembles the information entered by the user into a single data packet and sends it over the Internet to the server. The SSL / TLS protocol is used to ensure data integrity and security.

[0412] Input: Possession information (image data, basic information)

[0413] Output: Data packets sent to the server

[0414] Step 3:

[0415] The server analyzes the image and identifies the product. The server opens the received image data and analyzes the image using image analysis algorithms such as TensorFlow or PyTorch. The object's features (e.g., shape, color, logo) are extracted, and based on those features, a generative AI model is used to identify the product.

[0416] Input: Image data

[0417] Output: Identified product (e.g., laptop model Y from manufacturer X)

[0418] Step 4:

[0419] The server researches the market value of identified products on the Internet. The server uses web scraping tools such as BeautifulSoup and Scrapy to collect data from major e-commerce platforms. This allows the server to obtain the current market value, price fluctuation trends, past transaction history, etc. The server analyzes this data and calculates the average transaction price of the product.

[0420] Input: Identified product

[0421] Output: Market value data (current market value, price fluctuation trend, average transaction price)

[0422] Step 5:

[0423] The server notifies the user based on the research results. Based on the collected and analyzed market value data, the server uses a generative AI model to determine the optimal time to sell or replace the property. The results are communicated to the user via a pop-up notification or push notification on the app.

[0424] Input: Market value data, judgment results of the generated AI model

[0425] Output: Notification message (e.g., current market value is high, now is the time to sell)

[0426] Step 6:

[0427] The server provides users with after-sales support, suggests suitable selling platforms and potential buyers, automatically generates product descriptions and introductory text for selling items, and provides users with information on recycling and disposal methods.

[0428] Input: Product information that the user is selling, sales platform information

[0429] Output: Proposal details (selling platform, potential buyers, introduction, recycling information)

[0430] (Application example 1)

[0431] 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."

[0432] In modern society, people are required to properly manage unwanted items and let them go at the optimal time. However, many users find it difficult to accurately determine the market value of their belongings, making it difficult to find the right way to let go. Other issues include the time-consuming process of creating a sales description and selecting the optimal sales platform.

[0433] 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.

[0434] In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for automatically generating a sales description using a generative AI model, and means for suggesting selling the sales description on an optimal platform. This allows the user to accurately grasp the market value of their belongings and sell them at the optimal time, and also makes it easy to create a sales description and select a sales platform when selling.

[0435] "Belongings information" is detailed data about products and items that a user possesses, and includes image and text data.

[0436] "Means for identifying products" refers to a technical method for analyzing the entered belongings information and identifying specific product names and model names.

[0437] "Means for obtaining market value" refers to a technical method for obtaining the current market price or transaction price of a specified commodity based on data on the Internet.

[0438] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to a technological method for tracking fluctuations in market value in real time and informing users of the optimal timing for selling based on that information.

[0439] "Means to support the procedures for disposing of products" refers to technological methods that suggest ways for users to dispose of unwanted items, such as selling, transferring, or recycling, and facilitate the procedures.

[0440] A "generative AI model" is a machine learning model that uses artificial intelligence to automatically generate text, and has the ability to create sentences based on large amounts of data.

[0441] "Means for automatically generating sales descriptions" means a technical method for automatically generating sales descriptions highlighting the features and benefits of a product identified using a generative AI model.

[0442] The "means for suggesting the optimal platform for selling" refers to a technological method for recommending to a user the e-commerce platform on which the product can be most effectively sold based on the acquired market value data and product characteristics.

[0443] The present invention relates to a system for supporting the proper management and parting of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, a means for supporting procedures for disposing of the products, a means for automatically generating sales descriptions using a generative AI model, and a means for suggesting selling the sales descriptions on an optimal platform.

[0444] Specific explanation of program processing

[0445] 1. Enter your belongings information

[0446] Users use their smartphones to take a photo of the item they want to sell and upload it to the app, along with basic information such as the category, purchase date, and condition.

[0447] 2. Sending images and information

[0448] The device (smartphone) collects the uploaded images and the basic information entered into a data packet and sends it to a server over the Internet using HTTPS, a protocol that ensures secure data transmission.

[0449] 3. Image analysis for product identification

[0450] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to analyze the received images. Specifically, it identifies the product using an image classification model such as ResNet. In this step, it extracts product features from the image and identifies the specific product name and model name.

[0451] 4. Market Value Research

[0452] The server scrapes data from major e-commerce platforms (e.g., Amazon, eBay, major auction sites) to obtain market values ​​associated with identified products. For this purpose, scraping tools such as Beautiful Soup and Scrapy are used. The data is then analyzed to determine current market prices and price fluctuation trends.

[0453] 5. Notification of optimal timing

[0454] The server determines the optimal time to sell based on the acquired market value data and notifies the user. This notification is sent in real time via push notifications using Firebase Cloud Messaging.

[0455] 6. Auto-generated sales pitch

[0456] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the identified product's features and benefits. This description is an important factor in helping users sell their products at a higher price.

[0457] 7. Proposal of the best platform

[0458] The server proposes the most suitable sales platform for the user based on the acquired market value data and automatically generated product descriptions, allowing the user to easily find the best place to sell their products.

[0459] Specific examples

[0460] For example, consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone, uploads it to the app, and enters basic information such as when it was purchased and its condition. The device then sends this information to a server, which performs image analysis to identify the item. The server then researches market value from multiple online e-commerce platforms and notifies the user of the best time to sell. Furthermore, a generative AI model is used to automatically generate a sales description and suggests how to sell the item on the most suitable platform.

[0461] Examples of prompt statements

[0462] Examples of prompts include:

[0463] "Please generate a testimonial for a smartphone in good condition that you purchased in March 2021. Also, please suggest an effective way to sell it."

[0464] This allows users to easily maximize the value of their belongings and smoothly part with them in the right way.

[0465] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0466] Step 1:

[0467] The user uses their smartphone to take a photo of the item they want to get rid of and uploads it to the application. The input includes the product image and basic information (category, purchase date, condition). Based on this information, a data packet is generated to proceed to the next step.

[0468] Step 2:

[0469] The device (smartphone) combines the uploaded image and the entered basic information into a single data packet and sends it to the server via the Internet. The input is the product image and basic information data, and the output is a data packet sent to the server. This process uses the HTTPS protocol to ensure secure data transfer.

[0470] Step 3:

[0471] The server analyzes the received data packets and identifies the products using image analysis algorithms, such as TensorFlow or PyTorch, to extract features from the product images. The input is the image data and basic information, and the output is detailed information about the identified products (make, model, and other relevant information).

[0472] Step 4:

[0473] The server scrapes major e-commerce platforms to obtain the market value of the identified products. Tools such as Beautiful Soup and Scrapy are used to obtain price information published on the Internet. The input is product identification information, and the output is market value data (current market price, price fluctuation trend).

[0474] Step 5:

[0475] The server determines the optimal time to sell based on the acquired market value data. Here, an algorithm is used to analyze price fluctuation trends. The input is market value data, and the output is information on the optimal time to sell, which is notified to the user. This information is pushed in real time using Firebase Cloud Messaging.

[0476] Step 6:

[0477] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the features and benefits of the identified product. The input is product-specific information and basic information, and the output is the generated description. This description is then sent to the user's smartphone application.

[0478] Step 7:

[0479] The server recommends the most suitable sales platform for the user based on the acquired market value data and the automatically generated introduction. The input is the market value data and the generated introduction, and the output is information on the recommended sales platform. Based on this, the user can take specific steps to sell the product.

[0480] 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.

[0481] The present invention relates to a system that supports the appropriate management and disposal of unwanted items. The system includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining the market value of the identified products, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, and a means for supporting procedures for disposing of the products. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized notifications and advice.

[0482] Specific explanation of program processing

[0483] 1. The user takes a photo of an object and registers it

[0484] Users use their smartphone (device) camera to take a photo of the item they want to get rid of and upload it to a dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, current condition, etc.). At this time, the emotion engine also works, analyzing the user's facial expressions and voice to recognize their current emotion.

[0485] 2. The device sends the image to the server

[0486] The device combines the image uploaded by the user, the basic information entered, and the recognized emotion information into a single data packet and transmits it to a server via the Internet.

[0487] 3. The server analyzes the image and identifies the product

[0488] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0489] 4. The server retrieves detailed information about the identified product

[0490] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0491] 5. The server researches product value on the Internet

[0492] The server crawls major e-commerce platforms to obtain market values ​​related to the identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the server collects the market value of "Manufacturer A's Model X Laptop" from mail-order and auction sites and calculates the average transaction price.

[0493] 6. The server notifies you based on the research results.

[0494] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away. Furthermore, the server adjusts the content and timing of notifications based on the user's emotions as recognized by the emotion engine. For example, if the server notifies the user that "the current market value is high, so now is the time to sell," but the emotion engine recognizes that the user appears busy, the server will delay the notification.

[0495] 7. The server provides follow-up on how to let go

[0496] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the product and provides them to the user. It also provides information on recycling and disposal methods to the user. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0497] Specific examples

[0498] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo, basic information, and the user's emotional state to the server. The server analyzes the image to identify the item and research its market value. It takes the user's emotional state into consideration when notifying them of when a high price can be expected. Finally, the server supports the sale on the platform selected by the user, generates a description, and provides potential buyers.

[0499] Through this system, users can maximize the value of their items without much effort and declutter efficiently and appropriately.The incorporation of an emotion engine enables flexible responses according to the user's situation, increasing user satisfaction.

[0500] The processing flow will be explained below.

[0501] Step 1:

[0502] The user activates the smartphone camera and takes a photo of the item they want to declutter. The emotion engine then captures the user's face with the camera and recognizes their emotion from their facial expressions and voice. After taking the photo, the user opens the dedicated Danshari Agent app, uploads the photo, and also enters basic information such as the item's category, purchase date, and condition.

[0503] Step 2:

[0504] The terminal combines the image uploaded by the user, the input basic information, and the user's emotional information (e.g., relaxed, stressed, harmful, etc.) recognized by the emotion engine into a single data packet, which is then sent to a server via the Internet.

[0505] Step 3:

[0506] The server analyzes the received image using an image analysis algorithm. It extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server then identifies the product based on a machine learning model trained using the feature vector. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0507] Step 4:

[0508] The server retrieves detailed information about the identified item (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0509] Step 5:

[0510] The server crawls major e-commerce platforms to obtain the market value of a specified product. It collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server might determine that "the average transaction price on mail-order site B over the past month was X yen."

[0511] Step 6:

[0512] Based on the data collected and analyzed by the server, an algorithm determines the optimal time to sell, replace, or give away the vehicle. At the same time, it also takes into account the user's emotional state and adjusts the content and timing of notifications. For example, if the server determines that "the current market value is high, so now is the time to sell," it will notify the user immediately if the user is relaxed, or at an appropriate time if the user is feeling stressed.

[0513] Step 7:

[0514] The server supports the process of disposing of the product. This includes suggesting suitable selling platforms, suggesting potential buyers, and automatically generating a sales description and product description. The server also provides users with information on recycling and disposal methods. For example, "Automatically generate a sales description for a Model X laptop from manufacturer A on online shopping site C, and also provide information on high-price offers at second-hand shops."

[0515] This process allows users to get maximum benefit with minimum effort, and the emotion engine delivers personalized responses.

[0516] Example 2

[0517] 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."

[0518] In today's world, managing and disposing of personal possessions is difficult, especially when it comes to finding the right way to dispose of items that are no longer in use. It's also difficult to understand the market value of items and dispose of them at the optimal time. Furthermore, there's a problem with low user satisfaction, as notifications and advice aren't provided that take the user's feelings into consideration.

[0519] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting belongings information, a means for identifying an object based on the belongings information, a means for acquiring the market value of the identified object, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, a means for supporting procedures for disposing of the object, and a means for recognizing the user's emotions and adjusting the content and timing of the notification. This allows the user to easily manage and dispose of items, and dispose of them at the optimal time while maximizing the value of the items. Furthermore, personalized services that take emotions into consideration are provided, which can improve user satisfaction.

[0520] "Belongings information" is information about items owned by the user, and specifically includes detailed information such as category, purchase date, and current condition.

[0521] The "object" refers to an item that the user wishes to dispose of or manage, and is identified based on a photographed image and input information.

[0522] "Market value" refers to the value of an item when it is currently traded in the market, and is determined based on data obtained from e-commerce platforms, etc.

[0523] An "emotion engine" refers to technology or software that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0524] "Notification" refers to information sent from the server to the user, and includes information regarding fluctuations in market value and optimal disposal timing.

[0525] "Procedures" refers to the specific methods and procedures for disposing of the object, including support for actions such as selling, transferring, or discarding.

[0526] An "algorithm" refers to a set of calculation procedures or rules for solving a specific problem, and is used here to analyze market value and adjust notification timing.

[0527] This invention relates to a system that supports the proper management and disposal of unwanted items. This system works in conjunction with devices such as smartphones and tablets, and primarily assists in the input of belongings information, product identification, acquisition and monitoring of market value, timely notifications, and product disposal procedures. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized notifications and advice.

[0528] Hardware and software used

[0529] This system uses the following hardware and software:

[0530] Devices: smartphones, tablets

[0531] Server: Cloud server, database server

[0532] Software: Danshari agent app, image analysis algorithm, emotion engine, e-commerce platform crawling tool, generative AI model

[0533] Specific data processing and calculation

[0534] The user takes a photo of an object and registers the information.

[0535] Users use their device's camera to take a photo of the item they want to declutter and upload it to the dedicated Danshari Agent app. In addition to the photo, users enter basic information such as the category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice in real time to recognize their current emotional state.

[0536] The device sends the image and information to the server.

[0537] The device then combines the photo, basic information, and emotion information into a single data packet and transmits it to a server over the Internet using a secure communication protocol (e.g., HTTPS).

[0538] The server analyzes the image and identifies the product

[0539] The server analyzes the received image using an image analysis algorithm based on computer vision technology. The server extracts features such as shape, color, and brand logos from the image and generates a feature vector. This feature vector is then used to identify the relevant product based on a pre-trained machine learning model.

[0540] The server retrieves detailed information about the identified product.

[0541] The server retrieves detailed information about the identified product (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0542] The server researches product values ​​on the Internet

[0543] The server crawls major e-commerce platforms (e.g., Amazon, eBay) to obtain the market value of the identified product. It then collects and analyzes data such as market value, price fluctuation trends, and past transaction history to calculate the current market value.

[0544] The server will notify you based on the research results.

[0545] The server uses an algorithm to determine the optimal time to sell, replace, or give away. It also adjusts the content and timing of notifications based on the user's emotions as determined by the emotion engine. For example, the server may notify users that "the current market value is high, so now is the time to sell," but if the emotion engine determines that the user is busy, it will delay the notification.

[0546] The server will provide follow-up on how to let go

[0547] The server suggests suitable selling platforms and potential buyers, and also uses a generative AI model to automatically generate sales descriptions and product descriptions, as well as provide users with information on recycling and disposal methods.

[0548] Specific examples

[0549] When a user has an old smartphone they want to get rid of, they take a photo of it and upload it to the app. The device then sends the photo, basic information, and the user's emotional state to a server. The server analyzes the image to identify the item and researches its market value, notifying users when a high price can be expected. The server then supports the sale on the user's platform of choice, generates a sales description, and provides potential buyers, allowing users to efficiently dispose of their items.

[0550] Prompt Sentence Examples

[0551] "Please describe your system for helping people dispose of their old smartphones. Explain in detail how the system retrieves information from users' smartphones, uses that information to determine market value, and recommends the best time and method for selling."

[0552] This allows users to maximize the value of their items without much effort and declutter efficiently and appropriately.The emotion engine enables flexible responses tailored to the user's situation, which is expected to increase user satisfaction.

[0553] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] The user takes a photo of the item they want to declutter and uploads it using the device app. As input, the user provides basic information such as the image file, category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice to generate current emotion information. As output, the device combines the photo, basic information, and emotion information into a single data packet.

[0556] Step 2:

[0557] The device sends data to the server. As input, the device receives a data packet that it has assembled. The device transmits the data packet to the server over the Internet using a secure communication protocol (e.g., HTTPS). The output of this transmission process is the data received by the server.

[0558] Step 3:

[0559] The server extracts image data from the received data packets and performs image analysis. As input, it uses the image file and basic information sent to the server. Using computer vision techniques, the server extracts features such as shape, color, and brand logos in the image and generates a feature vector. As output, it obtains the feature vector and analyzed data.

[0560] Step 4:

[0561] The server identifies the product using the feature vector. The feature vector obtained by image analysis is used as input. The server inputs the feature vector into a machine learning model to identify the product. The output is detailed information about the identified product (manufacturer, model, specifications, etc.).

[0562] Step 5:

[0563] The server retrieves the identified product details from the database. Using the identified product details as input, the server executes a database query to retrieve the relevant details. The output is the product details retrieved from the database.

[0564] Step 6:

[0565] The server collects market value information from the Internet. It uses detailed information about the identified product as input. The server crawls major e-commerce platforms to collect data such as the product's price, transaction history, and price fluctuation trends. The output is the collected market value data.

[0566] Step 7:

[0567] The server analyzes the market value data and notifies the user based on the results. The collected market value data and sentiment information are used as input. The server uses an algorithm to determine the optimal timing for selling and adjusts the content and timing of the notification taking into account the user's sentiment information. The output is the adjusted content and timing of the notification.

[0568] Step 8:

[0569] The server provides the user with support on how to sell the item. As input, it uses the previously identified item details and market value data. The server suggests suitable selling platforms and potential buyers, and uses a generative AI model to automatically generate a listing and description for the item. It also provides information on recycling and disposal methods. The output is an automatically generated listing, a suggested selling platform, and advice on how to dispose of the item.

[0570] (Application example 2)

[0571] 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."

[0572] In modern society, many people find it difficult to properly manage and part with unnecessary items. This problem stems from a lack of information to understand the market value of items and decide when to part with them. Furthermore, the lack of personalized advice based on the user's emotional state makes it difficult to make decisions about decluttering. There is a growing need for a system that provides efficient and appropriate support to address these challenges.

[0573] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for recognizing the user's emotions and providing personalized notifications and advice, means for registering and managing products using a smartphone, means for identifying products using an image analysis algorithm, means for acquiring market values ​​from multiple e-commerce platforms on the Internet, and means for providing advice tailored to the user's situation using an emotion engine. This not only enables users to easily understand the market value of items and decide when to part with them, but also provides personalized advice tailored to the user's emotional state, thereby supporting the user's decision-making regarding decluttering.

[0574] - "Belongings Information" refers to detailed information about the items owned by the user, including the type of item, when it was purchased, its current condition, etc.

[0575] "Means for identifying the product" refers to a technical method for identifying the type of product an item is based on the entered belongings information.

[0576] "Means for obtaining market value" refers to a method for collecting the current market price or value of a specified commodity from data on the Internet.

[0577] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to technology that tracks trends in commodity market values ​​in real time and notifies users of the best time to sell or dispose of them.

[0578] "Means to support procedures for disposing of goods" refers to a system that provides users with information on how to choose a platform for selling goods, recycling methods, disposal procedures, etc.

[0579] "Means for recognizing a user's emotions and providing personalized notifications and advice" refers to technology that analyzes emotions from a user's facial expressions and voice, and uses that information to provide individually appropriate notifications and advice.

[0580] "Means for registering and managing products using a smartphone" refers to a method for entering and updating information about items owned by a user using a smartphone camera or application.

[0581] "Means for identifying products using image analysis algorithms" refers to technology that analyzes input image data and identifies the type of product the target item is based on the results.

[0582] "Means for obtaining market value from multiple e-commerce platforms on the Internet" refers to a method of collecting price information from multiple online marketplaces and calculating the market value of a product based on that data.

[0583] "Means for providing advice tailored to the user's situation using an emotion engine" refers to technology for providing advice with optimal timing and content based on the user's emotional state.

[0584] The present invention provides a system for supporting users in properly managing and letting go of their belongings, and is realized in the form of a smartphone application. The system includes the following means.

[0585] System Configuration

[0586] 1. How to enter your belongings:

[0587] Users take a photo of an item using their smartphone camera and upload it to the app, where they can enter basic information about the item (category, purchase date, current condition, etc.).

[0588] 2. Means of identifying the product:

[0589] The server uses image analysis algorithms to identify the item from the uploaded image, using computer vision techniques.

[0590] 3. Means of obtaining market value:

[0591] The server obtains the market value of the item from multiple e-commerce platforms on the Internet, and collects and analyzes market value data, price fluctuation trends, past transaction history, etc. related to the identified item.

[0592] 4. Means of monitoring market value fluctuations and notifying users in a timely manner:

[0593] The server uses an algorithm to determine the optimal time to sell based on the collected market value data and notifies the user. The content and timing of the notification also take into account the user's emotional state as recognized by an emotion engine.

[0594] 5. Supporting procedures for disposing of goods:

[0595] The server automatically generates and provides users with suitable selling platforms, potential buyers, and product descriptions for the items they are selling. It also provides information on recycling and disposal methods.

[0596] 6. How to recognize user emotions and provide personalized notifications and advice:

[0597] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and adjusts the content and timing of notifications based on the results.

[0598] Hardware and software used

[0599] Hardware:

[0600] Smartphone (camera, microphone, internet connection)

[0601] software:

[0602] Smartphone applications (e.g., Danshari Agent app)

[0603] Image analysis algorithms (e.g. OpenCV)

[0604] Machine learning models (e.g., scikit-learn)

[0605] Emotion Recognition Engine

[0606] Libraries for using data retrieval APIs (e.g., requests)

[0607] Specific examples

[0608] For example, if a user wants to get rid of an old smartphone, they can use the system as follows:

[0609] 1. The user takes a photo of their old smartphone and uploads it to the app. At the same time, they enter basic information about the item (category, purchase date, current condition, etc.). The prompt is: "I'd like to sell my old smartphone. I'll send you a photo so you can identify the item and tell me its market value. Also, please advise me on whether I should sell it now."

[0610] 2. The app sends the photo and basic information to a server, which uses image analysis algorithms to identify the item.

[0611] 3. The server obtains the market value of the identified item from multiple e-commerce platforms on the Internet and notifies the user of the optimal time to sell.

[0612] 4. An emotion engine analyzes the user's emotional state and provides personalized advice.

[0613] 5. Finally, we provide users with comprehensive support, including selecting a selling platform, automatically generating a sales description, and disposal methods such as recycling information.

[0614] In this way, the present invention allows users to maximize the value of their items efficiently and appropriately, making it easier for them to make decisions about decluttering.

[0615] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0616] Step 1:

[0617] Users take a photo of the item they want to get rid of with their smartphone and upload it to the app. They then enter basic information about the item, such as the category, purchase date, and current condition. The input data consists of a photo image file and basic text data.

[0618] Step 2:

[0619] The device combines the uploaded photo and basic information into a single data packet and sends it to the server. The input data is the image file and the basic information text data, and the output data is the data packet sent to the server.

[0620] Step 3:

[0621] The server analyzes the received images using an image analysis algorithm to identify the item. This process uses computer vision technology (e.g., OpenCV). The input data is the image file, and the output data is detailed information about the identified item (e.g., manufacturer, model, specifications, etc.).

[0622] Step 4:

[0623] The server retrieves detailed information of the identified item from the database and links it to the belongings information. The input data is the feature vector of the item, and the output data is detailed information of the item.

[0624] Step 5:

[0625] The server obtains market values ​​from multiple e-commerce platforms on the Internet. This process often uses web crawling technology. The input data is detailed information about the item, and the output data is market value data (e.g., current market price, price fluctuation trends, past transaction history, etc.).

[0626] Step 6:

[0627] The server analyzes the collected market value data and determines the optimal time to sell, replace, or give away the property based on an algorithm. The input data is the market value data, and the output data is the result of the decision on when to sell.

[0628] Step 7:

[0629] The server recognizes the user's emotions using an emotion engine and adjusts the content and timing of notifications. The input data is the user's facial expressions and voice data, and the output data is the result of emotion analysis.

[0630] Step 8:

[0631] The server takes into account the sentiment analysis results and sends appropriate notifications to the user. The notification content includes messages such as "Now is the time to sell because the current market value is high." The input data are the sentiment analysis results and market value data, and the output data is the notification message to the user.

[0632] Step 9:

[0633] The server provides after-sales support for disposing of items. Specifically, it selects a sales platform, automatically generates a description, and provides information on recycling and disposal methods. The input data is detailed information about the item and its market value, and the output data is after-sales support information for the user.

[0634] Through the above steps, the present invention can provide a system that enables users to maximize the value of items efficiently and appropriately and supports decision-making regarding decluttering.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Third embodiment]

[0639] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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."

[0651] The present invention relates to a system for supporting the appropriate management and disposal of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, and a means for supporting procedures for disposing of the products.

[0652] Specific explanation of program processing

[0653] 1. The user takes a photo of an object and registers it

[0654] Users use their smartphone camera to take a photo of the item they want to get rid of and upload it to the dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, condition, etc.).

[0655] 2. The device sends the image to the server

[0656] The terminal combines the images uploaded by the user and the basic information entered into a single data packet and transmits it to a server via the Internet.

[0657] 3. The server analyzes the image and identifies the product

[0658] The server analyzes the received image using an image analysis algorithm to extract the features of the object. Then, based on the extracted features, it identifies the product using a pre-trained machine learning model. For example, if a user registers an "old laptop," the server can analyze the image and identify it as a "Model X laptop from Manufacturer A."

[0659] 4. The server researches product value on the Internet

[0660] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the market value of "Manufacturer A's Model X Laptop" is collected from mail-order and auction sites, and the average transaction price is calculated.

[0661] 5. The server notifies you based on the research results.

[0662] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away the property, and notifies the user of the results. Notifications are sent as pop-ups on the app or push notifications. For example, the server might notify the user that "now is the time to sell, as the current market value is high."

[0663] 6. The server provides after-sales support on how to let go

[0664] The server suggests appropriate selling platforms and potential buyers. It also automatically generates product descriptions and descriptions for selling items and provides them to users. It also provides users with information on recycling and disposal methods. For example, the server automatically generates a product description for selling a "Model X laptop from Manufacturer A" on an online shopping site, and also provides information on high-price deals at second-hand shops.

[0665] Specific examples

[0666] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo and basic information to the server, which analyzes the image and identifies the item. The server then researches market value and notifies the user when a high price can be expected. Finally, the server helps the user sell the smartphone on the platform of their choice, generates a description, and provides potential buyers.

[0667] Through this system, users can easily maximize the value of their items and smoothly let go of them in the right way.

[0668] The processing flow will be explained below.

[0669] Step 1:

[0670] Users simply open their smartphone camera and take a photo of the item they want to declutter. After taking the photo, they open the dedicated Danshari Agent app and upload the photo to the app. They then fill in a form with basic information about the item, such as the category, purchase date, and current condition.

[0671] Step 2:

[0672] The terminal combines the images uploaded by the user and the basic information entered into a single data packet, which is then sent to a server via the Internet.

[0673] Step 3:

[0674] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0675] Step 4:

[0676] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0677] Step 5:

[0678] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. The server collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server obtains information such as "The average transaction price on mail-order site B over the past month was X yen."

[0679] Step 6:

[0680] Based on the data collected and analyzed by the server, an algorithm is used to determine the optimal time to sell, replace, or give away the property. The server generates a pop-up message or push notification in the app to notify the user of its results. For example, the server could notify the user that "now is the time to sell because the current market value is high."

[0681] Step 7:

[0682] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the item and provides them to the user. The server also provides information on recycling and disposal methods. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0683] This process allows users to maximize the value of their items without any hassle, and allows them to declutter efficiently and appropriately.

[0684] Example 1

[0685] 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."

[0686] In modern society, the number of items owned by individuals continues to increase, making the proper management and disposal of unwanted items an important issue. In particular, it is difficult for users to accurately grasp the market value of their belongings and to part with them at the optimal time. In this situation, there is a need for a method that reduces the burden on users and allows them to dispose of items efficiently.

[0687] 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.

[0688] In this invention, the server includes means for a user to input belongings information, means for transmitting the belongings information from a terminal to the server, means for the server to identify products based on the belongings information, means for obtaining market values ​​of the identified products from multiple e-commerce platforms on the Internet, means for monitoring fluctuations in the market values ​​and notifying the user at an appropriate time, and means for supporting procedures for disposing of the products and follow-up care, thereby enabling users to easily grasp the market values ​​of their belongings and let them go at the optimal time.

[0689] "User" refers to an individual who uses the system to manage and properly dispose of their belongings.

[0690] "Belonging information" is detailed information about an item that a user inputs into the system, including an image, category, purchase date, condition, and so on.

[0691] A "terminal" is a computer device that a user uses to input information about their belongings and send it to a server, and specifically refers to a smartphone, tablet, or the like.

[0692] "Server" refers to a central computer system that analyzes the received inventory information and provides functions such as identifying the item, obtaining its market value, and notifying the user.

[0693] "Image analysis algorithm" refers to a calculation method for extracting features of an item from an image received by the server and identifying the product based on that.

[0694] "Generative AI model" refers to an artificial intelligence model used to analyze detailed information about items identified after image analysis and determine the optimal time and method for users to let go of the item.

[0695] An "e-commerce platform" refers to a website or service where goods are bought and sold over the Internet, and is primarily the subject of market value data collection.

[0696] "Data packet" refers to a unit of data packed in a certain format used to transmit information over a network.

[0697] "Market value" refers to the prevailing price at which the identified item is traded in the current market, and is calculated based on transaction data obtained from e-commerce platforms.

[0698] "Notification" refers to the action taken by the server to inform the user of important information such as market value and the best time to sell, and can take the form of a pop-up display or push notification.

[0699] "Procedures and after-sales support" refers to a series of tasks and suggestions to support users in the process of letting go of their items, including automatically generating a sales description and suggesting suitable buyers.

[0700] This invention is a system that supports the proper management and disposal of unwanted items. This system is mainly composed of three elements: the user, the terminal, and the server.

[0701] First, the user takes a photo of the item they want to get rid of using their smartphone, tablet, or other device. Next, the user enters the photo along with basic information such as the category, purchase date, and current condition into a dedicated app, and sends it to the server via the app. The data sent is encrypted using the SSL / TLS protocol and delivered securely to the server.

[0702] Data sent from the device arrives at the server in the form of a data packet containing an image and basic information. The server first analyzes the received image using image analysis algorithms such as TensorFlow and PyTorch. This extracts the object's features (shape, color, manufacturer logo, etc.), and a generative AI model identifies the product based on these features. For example, if a user uploads an image of an old laptop, the server analyzes the image and identifies it as "Model X laptop from Manufacturer A."

[0703] Next, the server crawls major e-commerce platforms (e.g., Amazon, eBay, Yahoo! Auctions, etc.) to obtain the market value of the identified products. This crawling is performed using web scraping tools such as BeautifulSoup and Scrapy. The server collects data from each platform, such as the current market value, price fluctuation trends, and past transaction history, and analyzes this data to calculate the average transaction price of the product.

[0704] The server then uses the collected data and the generated AI model to determine the best time for the user to sell or replace the item. The results are communicated to the user via a pop-up notification or push notification on the app. For example, the server may notify the user, "The current market value is high, so now is the time to sell."

[0705] Finally, the server also has functions to support the process of letting go of items, such as suggesting suitable selling platforms and potential buyers, automatically generating product descriptions and descriptions for items, and providing information on recycling and disposal methods. This allows users to smoothly let go of their items and maximize their value.

[0706] Specific examples

[0707] When a user wants to sell their old smartphone, they first take a photo of it, upload it to a dedicated app, and enter basic information. The device then sends this data to a server, which analyzes the image and identifies the item. The server then researches the market value from e-commerce platforms and notifies the user of the best time to sell. Finally, the server automatically generates a sales description and suggests suitable selling platforms and potential buyers.

[0708] Prompt Sentence Examples

[0709] Here is an example of a prompt to input to a generative AI model:

[0710] "Research the market value of your old smartphone and, based on that, design a system that will tell you the best time to sell it."

[0711] Through this system, users can easily understand the value of their belongings and decide when to let them go.

[0712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0713] Step 1:

[0714] The user inputs information about their belongings. They use the device's camera to take a photo of the item they want to get rid of and upload it to a dedicated app. They also input basic information such as the category (e.g., electronic device), purchase date, and condition (e.g., used). The input data is saved in the app as image data and text data.

[0715] Input: Image data, basic information (category, purchase date, condition)

[0716] Output: Inventory information stored in the app

[0717] Step 2:

[0718] The device sends image data and basic information to the server. The device then assembles the information entered by the user into a single data packet and sends it over the Internet to the server. The SSL / TLS protocol is used to ensure data integrity and security.

[0719] Input: Possession information (image data, basic information)

[0720] Output: Data packets sent to the server

[0721] Step 3:

[0722] The server analyzes the image and identifies the product. The server opens the received image data and analyzes the image using image analysis algorithms such as TensorFlow or PyTorch. The object's features (e.g., shape, color, logo) are extracted, and based on those features, a generative AI model is used to identify the product.

[0723] Input: Image data

[0724] Output: Identified product (e.g., laptop model Y from manufacturer X)

[0725] Step 4:

[0726] The server researches the market value of identified products on the Internet. The server uses web scraping tools such as BeautifulSoup and Scrapy to collect data from major e-commerce platforms. This allows the server to obtain the current market value, price fluctuation trends, past transaction history, etc. The server analyzes this data and calculates the average transaction price of the product.

[0727] Input: Identified product

[0728] Output: Market value data (current market value, price fluctuation trend, average transaction price)

[0729] Step 5:

[0730] The server notifies the user based on the research results. Based on the collected and analyzed market value data, the server uses a generative AI model to determine the optimal time to sell or replace the property. The results are communicated to the user via a pop-up notification or push notification on the app.

[0731] Input: Market value data, judgment results of the generated AI model

[0732] Output: Notification message (e.g., current market value is high, now is the time to sell)

[0733] Step 6:

[0734] The server provides users with after-sales support, suggests suitable selling platforms and potential buyers, automatically generates product descriptions and introductory text for selling items, and provides users with information on recycling and disposal methods.

[0735] Input: Product information that the user is selling, sales platform information

[0736] Output: Proposal details (selling platform, potential buyers, introduction, recycling information)

[0737] (Application example 1)

[0738] 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."

[0739] In modern society, people are required to properly manage unwanted items and let them go at the optimal time. However, many users find it difficult to accurately determine the market value of their belongings, making it difficult to find the right way to let go. Other issues include the time-consuming process of creating a sales description and selecting the optimal sales platform.

[0740] 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.

[0741] In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for automatically generating a sales description using a generative AI model, and means for suggesting selling the sales description on an optimal platform. This allows the user to accurately grasp the market value of their belongings and sell them at the optimal time, and also makes it easy to create a sales description and select a sales platform when selling.

[0742] "Belongings information" is detailed data about products and items that a user possesses, and includes image and text data.

[0743] "Means for identifying products" refers to a technical method for analyzing the entered belongings information and identifying specific product names and model names.

[0744] "Means for obtaining market value" refers to a technical method for obtaining the current market price or transaction price of a specified commodity based on data on the Internet.

[0745] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to a technological method for tracking fluctuations in market value in real time and informing users of the optimal timing for selling based on that information.

[0746] "Means to support the procedures for disposing of products" refers to technological methods that suggest ways for users to dispose of unwanted items, such as selling, transferring, or recycling, and facilitate the procedures.

[0747] A "generative AI model" is a machine learning model that uses artificial intelligence to automatically generate text, and has the ability to create sentences based on large amounts of data.

[0748] "Means for automatically generating sales descriptions" means a technical method for automatically generating sales descriptions highlighting the features and benefits of a product identified using a generative AI model.

[0749] The "means for suggesting the optimal platform for selling" refers to a technological method for recommending to a user the e-commerce platform on which the product can be most effectively sold based on the acquired market value data and product characteristics.

[0750] The present invention relates to a system for supporting the proper management and parting of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, a means for supporting procedures for disposing of the products, a means for automatically generating sales descriptions using a generative AI model, and a means for suggesting selling the sales descriptions on an optimal platform.

[0751] Specific explanation of program processing

[0752] 1. Enter your belongings information

[0753] Users use their smartphones to take a photo of the item they want to sell and upload it to the app, along with basic information such as the category, purchase date, and condition.

[0754] 2. Sending images and information

[0755] The device (smartphone) collects the uploaded images and the basic information entered into a data packet and sends it to a server over the Internet using HTTPS, a protocol that ensures secure data transmission.

[0756] 3. Image analysis for product identification

[0757] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to analyze the received images. Specifically, it identifies the product using an image classification model such as ResNet. In this step, it extracts product features from the image and identifies the specific product name and model name.

[0758] 4. Market Value Research

[0759] The server scrapes data from major e-commerce platforms (e.g., Amazon, eBay, major auction sites) to obtain market values ​​associated with identified products. For this purpose, scraping tools such as Beautiful Soup and Scrapy are used. The data is then analyzed to determine current market prices and price fluctuation trends.

[0760] 5. Notification of optimal timing

[0761] The server determines the optimal time to sell based on the acquired market value data and notifies the user. This notification is sent in real time via push notifications using Firebase Cloud Messaging.

[0762] 6. Auto-generated sales pitch

[0763] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the identified product's features and benefits. This description is an important factor in helping users sell their products at a higher price.

[0764] 7. Proposal of the best platform

[0765] The server proposes the most suitable sales platform for the user based on the acquired market value data and automatically generated product descriptions, allowing the user to easily find the best place to sell their products.

[0766] Specific examples

[0767] For example, consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone, uploads it to the app, and enters basic information such as when it was purchased and its condition. The device then sends this information to a server, which performs image analysis to identify the item. The server then researches market value from multiple online e-commerce platforms and notifies the user of the best time to sell. Furthermore, a generative AI model is used to automatically generate a sales description and suggests how to sell the item on the most suitable platform.

[0768] Examples of prompt statements

[0769] Examples of prompts include:

[0770] "Please generate a testimonial for a smartphone in good condition that you purchased in March 2021. Also, please suggest an effective way to sell it."

[0771] This allows users to easily maximize the value of their belongings and smoothly part with them in the right way.

[0772] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0773] Step 1:

[0774] The user uses their smartphone to take a photo of the item they want to get rid of and uploads it to the application. The input includes the product image and basic information (category, purchase date, condition). Based on this information, a data packet is generated to proceed to the next step.

[0775] Step 2:

[0776] The device (smartphone) combines the uploaded image and the entered basic information into a single data packet and sends it to the server via the Internet. The input is the product image and basic information data, and the output is a data packet sent to the server. This process uses the HTTPS protocol to ensure secure data transfer.

[0777] Step 3:

[0778] The server analyzes the received data packets and identifies the products using image analysis algorithms, such as TensorFlow or PyTorch, to extract features from the product images. The input is the image data and basic information, and the output is detailed information about the identified products (make, model, and other relevant information).

[0779] Step 4:

[0780] The server scrapes major e-commerce platforms to obtain the market value of the identified products. Tools such as Beautiful Soup and Scrapy are used to obtain price information published on the Internet. The input is product identification information, and the output is market value data (current market price, price fluctuation trend).

[0781] Step 5:

[0782] The server determines the optimal time to sell based on the acquired market value data. Here, an algorithm is used to analyze price fluctuation trends. The input is market value data, and the output is information on the optimal time to sell, which is notified to the user. This information is pushed in real time using Firebase Cloud Messaging.

[0783] Step 6:

[0784] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the features and benefits of the identified product. The input is product-specific information and basic information, and the output is the generated description. This description is then sent to the user's smartphone application.

[0785] Step 7:

[0786] The server recommends the most suitable sales platform for the user based on the acquired market value data and the automatically generated introduction. The input is the market value data and the generated introduction, and the output is information on the recommended sales platform. Based on this, the user can take specific steps to sell the product.

[0787] 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.

[0788] The present invention relates to a system that supports the appropriate management and disposal of unwanted items. The system includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining the market value of the identified products, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, and a means for supporting procedures for disposing of the products. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized notifications and advice.

[0789] Specific explanation of program processing

[0790] 1. The user takes a photo of an object and registers it

[0791] Users use their smartphone (device) camera to take a photo of the item they want to get rid of and upload it to a dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, current condition, etc.). At this time, the emotion engine also works, analyzing the user's facial expressions and voice to recognize their current emotion.

[0792] 2. The device sends the image to the server

[0793] The device combines the image uploaded by the user, the basic information entered, and the recognized emotion information into a single data packet and transmits it to a server via the Internet.

[0794] 3. The server analyzes the image and identifies the product

[0795] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0796] 4. The server retrieves detailed information about the identified product

[0797] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0798] 5. The server researches product value on the Internet

[0799] The server crawls major e-commerce platforms to obtain market values ​​related to the identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the server collects the market value of "Manufacturer A's Model X Laptop" from mail-order and auction sites and calculates the average transaction price.

[0800] 6. The server notifies you based on the research results.

[0801] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away. Furthermore, the server adjusts the content and timing of notifications based on the user's emotions as recognized by the emotion engine. For example, if the server notifies the user that "the current market value is high, so now is the time to sell," but the emotion engine recognizes that the user appears busy, the server will delay the notification.

[0802] 7. The server provides follow-up on how to let go

[0803] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the product and provides them to the user. It also provides information on recycling and disposal methods to the user. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0804] Specific examples

[0805] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo, basic information, and the user's emotional state to the server. The server analyzes the image to identify the item and research its market value. It takes the user's emotional state into consideration when notifying them of when a high price can be expected. Finally, the server supports the sale on the platform selected by the user, generates a description, and provides potential buyers.

[0806] Through this system, users can maximize the value of their items without much effort and declutter efficiently and appropriately.The incorporation of an emotion engine enables flexible responses according to the user's situation, increasing user satisfaction.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] The user activates the smartphone camera and takes a photo of the item they want to declutter. The emotion engine then captures the user's face with the camera and recognizes their emotion from their facial expressions and voice. After taking the photo, the user opens the dedicated Danshari Agent app, uploads the photo, and also enters basic information such as the item's category, purchase date, and condition.

[0810] Step 2:

[0811] The terminal combines the image uploaded by the user, the input basic information, and the user's emotional information (e.g., relaxed, stressed, harmful, etc.) recognized by the emotion engine into a single data packet, which is then sent to a server via the Internet.

[0812] Step 3:

[0813] The server analyzes the received image using an image analysis algorithm. It extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server then identifies the product based on a machine learning model trained using the feature vector. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0814] Step 4:

[0815] The server retrieves detailed information about the identified item (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0816] Step 5:

[0817] The server crawls major e-commerce platforms to obtain the market value of a specified product. It collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server might determine that "the average transaction price on mail-order site B over the past month was X yen."

[0818] Step 6:

[0819] Based on the data collected and analyzed by the server, an algorithm determines the optimal time to sell, replace, or give away the vehicle. At the same time, it also takes into account the user's emotional state and adjusts the content and timing of notifications. For example, if the server determines that "the current market value is high, so now is the time to sell," it will notify the user immediately if the user is relaxed, or at an appropriate time if the user is feeling stressed.

[0820] Step 7:

[0821] The server supports the process of disposing of the product. This includes suggesting suitable selling platforms, suggesting potential buyers, and automatically generating a sales description and product description. The server also provides users with information on recycling and disposal methods. For example, "Automatically generate a sales description for a Model X laptop from manufacturer A on online shopping site C, and also provide information on high-price offers at second-hand shops."

[0822] This process allows users to get maximum benefit with minimum effort, and the emotion engine delivers personalized responses.

[0823] Example 2

[0824] 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."

[0825] In today's world, managing and disposing of personal possessions is difficult, especially when it comes to finding the right way to dispose of items that are no longer in use. It's also difficult to understand the market value of items and dispose of them at the optimal time. Furthermore, there's a problem with low user satisfaction, as notifications and advice aren't provided that take the user's feelings into consideration.

[0826] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting belongings information, a means for identifying an object based on the belongings information, a means for acquiring the market value of the identified object, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, a means for supporting procedures for disposing of the object, and a means for recognizing the user's emotions and adjusting the content and timing of the notification. This allows the user to easily manage and dispose of items, and dispose of them at the optimal time while maximizing the value of the items. Furthermore, personalized services that take emotions into consideration are provided, which can improve user satisfaction.

[0827] "Belongings information" is information about items owned by the user, and specifically includes detailed information such as category, purchase date, and current condition.

[0828] The "object" refers to an item that the user wishes to dispose of or manage, and is identified based on a photographed image and input information.

[0829] "Market value" refers to the value of an item when it is currently traded in the market, and is determined based on data obtained from e-commerce platforms, etc.

[0830] An "emotion engine" refers to technology or software that analyzes a user's facial expressions and voice to recognize their current emotional state.

[0831] "Notification" refers to information sent from the server to the user, and includes information regarding fluctuations in market value and optimal disposal timing.

[0832] "Procedures" refers to the specific methods and procedures for disposing of the object, including support for actions such as selling, transferring, or discarding.

[0833] An "algorithm" refers to a set of calculation procedures or rules for solving a specific problem, and is used here to analyze market value and adjust notification timing.

[0834] This invention relates to a system that supports the proper management and disposal of unwanted items. This system works in conjunction with devices such as smartphones and tablets, and primarily assists in the input of belongings information, product identification, acquisition and monitoring of market value, timely notifications, and product disposal procedures. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized notifications and advice.

[0835] Hardware and software used

[0836] This system uses the following hardware and software:

[0837] Devices: smartphones, tablets

[0838] Server: Cloud server, database server

[0839] Software: Danshari agent app, image analysis algorithm, emotion engine, e-commerce platform crawling tool, generative AI model

[0840] Specific data processing and calculation

[0841] The user takes a photo of an object and registers the information.

[0842] Users use their device's camera to take a photo of the item they want to declutter and upload it to the dedicated Danshari Agent app. In addition to the photo, users enter basic information such as the category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice in real time to recognize their current emotional state.

[0843] The device sends the image and information to the server.

[0844] The device then combines the photo, basic information, and emotion information into a single data packet and transmits it to a server over the Internet using a secure communication protocol (e.g., HTTPS).

[0845] The server analyzes the image and identifies the product

[0846] The server analyzes the received image using an image analysis algorithm based on computer vision technology. The server extracts features such as shape, color, and brand logos from the image and generates a feature vector. This feature vector is then used to identify the relevant product based on a pre-trained machine learning model.

[0847] The server retrieves detailed information about the identified product.

[0848] The server retrieves detailed information about the identified product (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[0849] The server researches product values ​​on the Internet

[0850] The server crawls major e-commerce platforms (e.g., Amazon, eBay) to obtain the market value of the identified product. It then collects and analyzes data such as market value, price fluctuation trends, and past transaction history to calculate the current market value.

[0851] The server will notify you based on the research results.

[0852] The server uses an algorithm to determine the optimal time to sell, replace, or give away. It also adjusts the content and timing of notifications based on the user's emotions as determined by the emotion engine. For example, the server may notify users that "the current market value is high, so now is the time to sell," but if the emotion engine determines that the user is busy, it will delay the notification.

[0853] The server will provide follow-up on how to let go

[0854] The server suggests suitable selling platforms and potential buyers, and also uses a generative AI model to automatically generate sales descriptions and product descriptions, as well as provide users with information on recycling and disposal methods.

[0855] Specific examples

[0856] When a user has an old smartphone they want to get rid of, they take a photo of it and upload it to the app. The device then sends the photo, basic information, and the user's emotional state to a server. The server analyzes the image to identify the item and researches its market value, notifying users when a high price can be expected. The server then supports the sale on the user's platform of choice, generates a sales description, and provides potential buyers, allowing users to efficiently dispose of their items.

[0857] Prompt Sentence Examples

[0858] "Please describe your system for helping people dispose of their old smartphones. Explain in detail how the system retrieves information from users' smartphones, uses that information to determine market value, and recommends the best time and method for selling."

[0859] This allows users to maximize the value of their items without much effort and declutter efficiently and appropriately.The emotion engine enables flexible responses tailored to the user's situation, which is expected to increase user satisfaction.

[0860] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0861] Step 1:

[0862] The user takes a photo of the item they want to declutter and uploads it using the device app. As input, the user provides basic information such as the image file, category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice to generate current emotion information. As output, the device combines the photo, basic information, and emotion information into a single data packet.

[0863] Step 2:

[0864] The device sends data to the server. As input, the device receives a data packet that it has assembled. The device transmits the data packet to the server over the Internet using a secure communication protocol (e.g., HTTPS). The output of this transmission process is the data received by the server.

[0865] Step 3:

[0866] The server extracts image data from the received data packets and performs image analysis. As input, it uses the image file and basic information sent to the server. Using computer vision techniques, the server extracts features such as shape, color, and brand logos in the image and generates a feature vector. As output, it obtains the feature vector and analyzed data.

[0867] Step 4:

[0868] The server identifies the product using the feature vector. The feature vector obtained by image analysis is used as input. The server inputs the feature vector into a machine learning model to identify the product. The output is detailed information about the identified product (manufacturer, model, specifications, etc.).

[0869] Step 5:

[0870] The server retrieves the identified product details from the database. Using the identified product details as input, the server executes a database query to retrieve the relevant details. The output is the product details retrieved from the database.

[0871] Step 6:

[0872] The server collects market value information from the Internet. It uses detailed information about the identified product as input. The server crawls major e-commerce platforms to collect data such as the product's price, transaction history, and price fluctuation trends. The output is the collected market value data.

[0873] Step 7:

[0874] The server analyzes the market value data and notifies the user based on the results. The collected market value data and sentiment information are used as input. The server uses an algorithm to determine the optimal timing for selling and adjusts the content and timing of the notification taking into account the user's sentiment information. The output is the adjusted content and timing of the notification.

[0875] Step 8:

[0876] The server provides the user with support on how to sell the item. As input, it uses the previously identified item details and market value data. The server suggests suitable selling platforms and potential buyers, and uses a generative AI model to automatically generate a listing and description for the item. It also provides information on recycling and disposal methods. The output is an automatically generated listing, a suggested selling platform, and advice on how to dispose of the item.

[0877] (Application example 2)

[0878] 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."

[0879] In modern society, many people find it difficult to properly manage and part with unnecessary items. This problem stems from a lack of information to understand the market value of items and decide when to part with them. Furthermore, the lack of personalized advice based on the user's emotional state makes it difficult to make decisions about decluttering. There is a growing need for a system that provides efficient and appropriate support to address these challenges.

[0880] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for recognizing the user's emotions and providing personalized notifications and advice, means for registering and managing products using a smartphone, means for identifying products using an image analysis algorithm, means for acquiring market values ​​from multiple e-commerce platforms on the Internet, and means for providing advice tailored to the user's situation using an emotion engine. This not only enables users to easily understand the market value of items and decide when to part with them, but also provides personalized advice tailored to the user's emotional state, thereby supporting the user's decision-making regarding decluttering.

[0881] - "Belongings Information" refers to detailed information about the items owned by the user, including the type of item, when it was purchased, its current condition, etc.

[0882] "Means for identifying the product" refers to a technical method for identifying the type of product an item is based on the entered belongings information.

[0883] "Means for obtaining market value" refers to a method for collecting the current market price or value of a specified commodity from data on the Internet.

[0884] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to technology that tracks trends in commodity market values ​​in real time and notifies users of the best time to sell or dispose of them.

[0885] "Means to support procedures for disposing of goods" refers to a system that provides users with information on how to choose a platform for selling goods, recycling methods, disposal procedures, etc.

[0886] "Means for recognizing a user's emotions and providing personalized notifications and advice" refers to technology that analyzes emotions from a user's facial expressions and voice, and uses that information to provide individually appropriate notifications and advice.

[0887] "Means for registering and managing products using a smartphone" refers to a method for entering and updating information about items owned by a user using a smartphone camera or application.

[0888] "Means for identifying products using image analysis algorithms" refers to technology that analyzes input image data and identifies the type of product the target item is based on the results.

[0889] "Means for obtaining market value from multiple e-commerce platforms on the Internet" refers to a method of collecting price information from multiple online marketplaces and calculating the market value of a product based on that data.

[0890] "Means for providing advice tailored to the user's situation using an emotion engine" refers to technology for providing advice with optimal timing and content based on the user's emotional state.

[0891] The present invention provides a system for supporting users in properly managing and letting go of their belongings, and is realized in the form of a smartphone application. The system includes the following means.

[0892] System Configuration

[0893] 1. How to enter your belongings:

[0894] Users take a photo of an item using their smartphone camera and upload it to the app, where they can enter basic information about the item (category, purchase date, current condition, etc.).

[0895] 2. Means of identifying the product:

[0896] The server uses image analysis algorithms to identify the item from the uploaded image, using computer vision techniques.

[0897] 3. Means of obtaining market value:

[0898] The server obtains the market value of the item from multiple e-commerce platforms on the Internet, and collects and analyzes market value data, price fluctuation trends, past transaction history, etc. related to the identified item.

[0899] 4. Means of monitoring market value fluctuations and notifying users in a timely manner:

[0900] The server uses an algorithm to determine the optimal time to sell based on the collected market value data and notifies the user. The content and timing of the notification also take into account the user's emotional state as recognized by an emotion engine.

[0901] 5. Supporting procedures for disposing of goods:

[0902] The server automatically generates and provides users with suitable selling platforms, potential buyers, and product descriptions for the items they are selling. It also provides information on recycling and disposal methods.

[0903] 6. How to recognize user emotions and provide personalized notifications and advice:

[0904] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and adjusts the content and timing of notifications based on the results.

[0905] Hardware and software used

[0906] Hardware:

[0907] Smartphone (camera, microphone, internet connection)

[0908] software:

[0909] Smartphone applications (e.g., Danshari Agent app)

[0910] Image analysis algorithms (e.g. OpenCV)

[0911] Machine learning models (e.g., scikit-learn)

[0912] Emotion Recognition Engine

[0913] Libraries for using data retrieval APIs (e.g., requests)

[0914] Specific examples

[0915] For example, if a user wants to get rid of an old smartphone, they can use the system as follows:

[0916] 1. The user takes a photo of their old smartphone and uploads it to the app. At the same time, they enter basic information about the item (category, purchase date, current condition, etc.). The prompt is: "I'd like to sell my old smartphone. I'll send you a photo so you can identify the item and tell me its market value. Also, please advise me on whether I should sell it now."

[0917] 2. The app sends the photo and basic information to a server, which uses image analysis algorithms to identify the item.

[0918] 3. The server obtains the market value of the identified item from multiple e-commerce platforms on the Internet and notifies the user of the optimal time to sell.

[0919] 4. An emotion engine analyzes the user's emotional state and provides personalized advice.

[0920] 5. Finally, we provide users with comprehensive support, including selecting a selling platform, automatically generating a sales description, and disposal methods such as recycling information.

[0921] In this way, the present invention allows users to maximize the value of their items efficiently and appropriately, making it easier for them to make decisions about decluttering.

[0922] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0923] Step 1:

[0924] Users take a photo of the item they want to get rid of with their smartphone and upload it to the app. They then enter basic information about the item, such as the category, purchase date, and current condition. The input data consists of a photo image file and basic text data.

[0925] Step 2:

[0926] The device combines the uploaded photo and basic information into a single data packet and sends it to the server. The input data is the image file and the basic information text data, and the output data is the data packet sent to the server.

[0927] Step 3:

[0928] The server analyzes the received images using an image analysis algorithm to identify the item. This process uses computer vision technology (e.g., OpenCV). The input data is the image file, and the output data is detailed information about the identified item (e.g., manufacturer, model, specifications, etc.).

[0929] Step 4:

[0930] The server retrieves detailed information of the identified item from the database and links it to the belongings information. The input data is the feature vector of the item, and the output data is detailed information of the item.

[0931] Step 5:

[0932] The server obtains market values ​​from multiple e-commerce platforms on the Internet. This process often uses web crawling technology. The input data is detailed information about the item, and the output data is market value data (e.g., current market price, price fluctuation trends, past transaction history, etc.).

[0933] Step 6:

[0934] The server analyzes the collected market value data and determines the optimal time to sell, replace, or give away the property based on an algorithm. The input data is the market value data, and the output data is the result of the decision on when to sell.

[0935] Step 7:

[0936] The server recognizes the user's emotions using an emotion engine and adjusts the content and timing of notifications. The input data is the user's facial expressions and voice data, and the output data is the result of emotion analysis.

[0937] Step 8:

[0938] The server takes into account the sentiment analysis results and sends appropriate notifications to the user. The notification content includes messages such as "Now is the time to sell because the current market value is high." The input data are the sentiment analysis results and market value data, and the output data is the notification message to the user.

[0939] Step 9:

[0940] The server provides after-sales support for disposing of items. Specifically, it selects a sales platform, automatically generates a description, and provides information on recycling and disposal methods. The input data is detailed information about the item and its market value, and the output data is after-sales support information for the user.

[0941] Through the above steps, the present invention can provide a system that enables users to maximize the value of items efficiently and appropriately and supports decision-making regarding decluttering.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] [Fourth embodiment]

[0946] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0947] 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.

[0948] 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).

[0949] 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.

[0950] 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.

[0951] 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).

[0952] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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."

[0959] The present invention relates to a system for supporting the appropriate management and disposal of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, and a means for supporting procedures for disposing of the products.

[0960] Specific explanation of program processing

[0961] 1. The user takes a photo of an object and registers it

[0962] Users use their smartphone camera to take a photo of the item they want to get rid of and upload it to the dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, condition, etc.).

[0963] 2. The device sends the image to the server

[0964] The terminal combines the images uploaded by the user and the basic information entered into a single data packet and transmits it to a server via the Internet.

[0965] 3. The server analyzes the image and identifies the product

[0966] The server analyzes the received image using an image analysis algorithm to extract the features of the object. Then, based on the extracted features, it identifies the product using a pre-trained machine learning model. For example, if a user registers an "old laptop," the server can analyze the image and identify it as a "Model X laptop from Manufacturer A."

[0967] 4. The server researches product value on the Internet

[0968] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the market value of "Manufacturer A's Model X Laptop" is collected from mail-order and auction sites, and the average transaction price is calculated.

[0969] 5. The server notifies you based on the research results.

[0970] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away the property, and notifies the user of the results. Notifications are sent as pop-ups on the app or push notifications. For example, the server might notify the user that "now is the time to sell, as the current market value is high."

[0971] 6. The server provides after-sales support on how to let go

[0972] The server suggests appropriate selling platforms and potential buyers. It also automatically generates product descriptions and descriptions for selling items and provides them to users. It also provides users with information on recycling and disposal methods. For example, the server automatically generates a product description for selling a "Model X laptop from Manufacturer A" on an online shopping site, and also provides information on high-price deals at second-hand shops.

[0973] Specific examples

[0974] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo and basic information to the server, which analyzes the image and identifies the item. The server then researches market value and notifies the user when a high price can be expected. Finally, the server helps the user sell the smartphone on the platform of their choice, generates a description, and provides potential buyers.

[0975] Through this system, users can easily maximize the value of their items and smoothly let go of them in the right way.

[0976] The processing flow will be explained below.

[0977] Step 1:

[0978] Users simply open their smartphone camera and take a photo of the item they want to declutter. After taking the photo, they open the dedicated Danshari Agent app and upload the photo to the app. They then fill in a form with basic information about the item, such as the category, purchase date, and current condition.

[0979] Step 2:

[0980] The terminal combines the images uploaded by the user and the basic information entered into a single data packet, which is then sent to a server via the Internet.

[0981] Step 3:

[0982] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[0983] Step 4:

[0984] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[0985] Step 5:

[0986] The server crawls major e-commerce platforms to obtain market values ​​related to identified products. The server collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server obtains information such as "The average transaction price on mail-order site B over the past month was X yen."

[0987] Step 6:

[0988] Based on the data collected and analyzed by the server, an algorithm is used to determine the optimal time to sell, replace, or give away the property. The server generates a pop-up message or push notification in the app to notify the user of its results. For example, the server could notify the user that "now is the time to sell because the current market value is high."

[0989] Step 7:

[0990] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the item and provides them to the user. The server also provides information on recycling and disposal methods. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[0991] This process allows users to maximize the value of their items without any hassle, and allows them to declutter efficiently and appropriately.

[0992] Example 1

[0993] 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."

[0994] In modern society, the number of items owned by individuals continues to increase, making the proper management and disposal of unwanted items an important issue. In particular, it is difficult for users to accurately grasp the market value of their belongings and to part with them at the optimal time. In this situation, there is a need for a method that reduces the burden on users and allows them to dispose of items efficiently.

[0995] 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.

[0996] In this invention, the server includes means for a user to input belongings information, means for transmitting the belongings information from a terminal to the server, means for the server to identify products based on the belongings information, means for obtaining market values ​​of the identified products from multiple e-commerce platforms on the Internet, means for monitoring fluctuations in the market values ​​and notifying the user at an appropriate time, and means for supporting procedures for disposing of the products and follow-up care, thereby enabling users to easily grasp the market values ​​of their belongings and let them go at the optimal time.

[0997] "User" refers to an individual who uses the system to manage and properly dispose of their belongings.

[0998] "Belonging information" is detailed information about an item that a user inputs into the system, including an image, category, purchase date, condition, and so on.

[0999] A "terminal" is a computer device that a user uses to input information about their belongings and send it to a server, and specifically refers to a smartphone, tablet, or the like.

[1000] "Server" refers to a central computer system that analyzes the received inventory information and provides functions such as identifying the item, obtaining its market value, and notifying the user.

[1001] "Image analysis algorithm" refers to a calculation method for extracting features of an item from an image received by the server and identifying the product based on that.

[1002] "Generative AI model" refers to an artificial intelligence model used to analyze detailed information about items identified after image analysis and determine the optimal time and method for users to let go of the item.

[1003] An "e-commerce platform" refers to a website or service where goods are bought and sold over the Internet, and is primarily the subject of market value data collection.

[1004] "Data packet" refers to a unit of data packed in a certain format used to transmit information over a network.

[1005] "Market value" refers to the prevailing price at which the identified item is traded in the current market, and is calculated based on transaction data obtained from e-commerce platforms.

[1006] "Notification" refers to the action taken by the server to inform the user of important information such as market value and the best time to sell, and can take the form of a pop-up display or push notification.

[1007] "Procedures and after-sales support" refers to a series of tasks and suggestions to support users in the process of letting go of their items, including automatically generating a sales description and suggesting suitable buyers.

[1008] This invention is a system that supports the proper management and disposal of unwanted items. This system is mainly composed of three elements: the user, the terminal, and the server.

[1009] First, the user takes a photo of the item they want to get rid of using their smartphone, tablet, or other device. Next, the user enters the photo along with basic information such as the category, purchase date, and current condition into a dedicated app, and sends it to the server via the app. The data sent is encrypted using the SSL / TLS protocol and delivered securely to the server.

[1010] Data sent from the device arrives at the server in the form of a data packet containing an image and basic information. The server first analyzes the received image using image analysis algorithms such as TensorFlow and PyTorch. This extracts the object's features (shape, color, manufacturer logo, etc.), and a generative AI model identifies the product based on these features. For example, if a user uploads an image of an old laptop, the server analyzes the image and identifies it as "Model X laptop from Manufacturer A."

[1011] Next, the server crawls major e-commerce platforms (e.g., Amazon, eBay, Yahoo! Auctions, etc.) to obtain the market value of the identified products. This crawling is performed using web scraping tools such as BeautifulSoup and Scrapy. The server collects data from each platform, such as the current market value, price fluctuation trends, and past transaction history, and analyzes this data to calculate the average transaction price of the product.

[1012] The server then uses the collected data and the generated AI model to determine the best time for the user to sell or replace the item. The results are communicated to the user via a pop-up notification or push notification on the app. For example, the server may notify the user, "The current market value is high, so now is the time to sell."

[1013] Finally, the server also has functions to support the process of letting go of items, such as suggesting suitable selling platforms and potential buyers, automatically generating product descriptions and descriptions for items, and providing information on recycling and disposal methods. This allows users to smoothly let go of their items and maximize their value.

[1014] Specific examples

[1015] When a user wants to sell their old smartphone, they first take a photo of it, upload it to a dedicated app, and enter basic information. The device then sends this data to a server, which analyzes the image and identifies the item. The server then researches the market value from e-commerce platforms and notifies the user of the best time to sell. Finally, the server automatically generates a sales description and suggests suitable selling platforms and potential buyers.

[1016] Prompt Sentence Examples

[1017] Here is an example of a prompt to input to a generative AI model:

[1018] "Research the market value of your old smartphone and, based on that, design a system that will tell you the best time to sell it."

[1019] Through this system, users can easily understand the value of their belongings and decide when to let them go.

[1020] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1021] Step 1:

[1022] The user inputs information about their belongings. They use the device's camera to take a photo of the item they want to get rid of and upload it to a dedicated app. They also input basic information such as the category (e.g., electronic device), purchase date, and condition (e.g., used). The input data is saved in the app as image data and text data.

[1023] Input: Image data, basic information (category, purchase date, condition)

[1024] Output: Inventory information stored in the app

[1025] Step 2:

[1026] The device sends image data and basic information to the server. The device then assembles the information entered by the user into a single data packet and sends it over the Internet to the server. The SSL / TLS protocol is used to ensure data integrity and security.

[1027] Input: Possession information (image data, basic information)

[1028] Output: Data packets sent to the server

[1029] Step 3:

[1030] The server analyzes the image and identifies the product. The server opens the received image data and analyzes the image using image analysis algorithms such as TensorFlow or PyTorch. The object's features (e.g., shape, color, logo) are extracted, and based on those features, a generative AI model is used to identify the product.

[1031] Input: Image data

[1032] Output: Identified product (e.g., laptop model Y from manufacturer X)

[1033] Step 4:

[1034] The server researches the market value of identified products on the Internet. The server uses web scraping tools such as BeautifulSoup and Scrapy to collect data from major e-commerce platforms. This allows the server to obtain the current market value, price fluctuation trends, past transaction history, etc. The server analyzes this data and calculates the average transaction price of the product.

[1035] Input: Identified product

[1036] Output: Market value data (current market value, price fluctuation trend, average transaction price)

[1037] Step 5:

[1038] The server notifies the user based on the research results. Based on the collected and analyzed market value data, the server uses a generative AI model to determine the optimal time to sell or replace the property. The results are communicated to the user via a pop-up notification or push notification on the app.

[1039] Input: Market value data, judgment results of the generated AI model

[1040] Output: Notification message (e.g., current market value is high, now is the time to sell)

[1041] Step 6:

[1042] The server provides users with after-sales support, suggests suitable selling platforms and potential buyers, automatically generates product descriptions and introductory text for selling items, and provides users with information on recycling and disposal methods.

[1043] Input: Product information that the user is selling, sales platform information

[1044] Output: Proposal details (selling platform, potential buyers, introduction, recycling information)

[1045] (Application example 1)

[1046] 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."

[1047] In modern society, people are required to properly manage unwanted items and let them go at the optimal time. However, many users find it difficult to accurately determine the market value of their belongings, making it difficult to find the right way to let go. Other issues include the time-consuming process of creating a sales description and selecting the optimal sales platform.

[1048] 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.

[1049] In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for automatically generating a sales description using a generative AI model, and means for suggesting selling the sales description on an optimal platform. This allows the user to accurately grasp the market value of their belongings and sell them at the optimal time, and also makes it easy to create a sales description and select a sales platform when selling.

[1050] "Belongings information" is detailed data about products and items that a user possesses, and includes image and text data.

[1051] "Means for identifying products" refers to a technical method for analyzing the entered belongings information and identifying specific product names and model names.

[1052] "Means for obtaining market value" refers to a technical method for obtaining the current market price or transaction price of a specified commodity based on data on the Internet.

[1053] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to a technological method for tracking fluctuations in market value in real time and informing users of the optimal timing for selling based on that information.

[1054] "Means to support the procedures for disposing of products" refers to technological methods that suggest ways for users to dispose of unwanted items, such as selling, transferring, or recycling, and facilitate the procedures.

[1055] A "generative AI model" is a machine learning model that uses artificial intelligence to automatically generate text, and has the ability to create sentences based on large amounts of data.

[1056] "Means for automatically generating sales descriptions" means a technical method for automatically generating sales descriptions highlighting the features and benefits of a product identified using a generative AI model.

[1057] The "means for suggesting the optimal platform for selling" refers to a technological method for recommending to a user the e-commerce platform on which the product can be most effectively sold based on the acquired market value data and product characteristics.

[1058] The present invention relates to a system for supporting the proper management and parting of unwanted items, which includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining market values ​​of the identified products, a means for monitoring fluctuations in the market values ​​and notifying a user at an appropriate time, a means for supporting procedures for disposing of the products, a means for automatically generating sales descriptions using a generative AI model, and a means for suggesting selling the sales descriptions on an optimal platform.

[1059] Specific explanation of program processing

[1060] 1. Enter your belongings information

[1061] Users use their smartphones to take a photo of the item they want to sell and upload it to the app, along with basic information such as the category, purchase date, and condition.

[1062] 2. Sending images and information

[1063] The device (smartphone) collects the uploaded images and the basic information entered into a data packet and sends it to a server over the Internet using HTTPS, a protocol that ensures secure data transmission.

[1064] 3. Image analysis for product identification

[1065] The server uses a machine learning framework (e.g., TensorFlow, PyTorch) to analyze the received images. Specifically, it identifies the product using an image classification model such as ResNet. In this step, it extracts product features from the image and identifies the specific product name and model name.

[1066] 4. Market Value Research

[1067] The server scrapes data from major e-commerce platforms (e.g., Amazon, eBay, major auction sites) to obtain market values ​​associated with identified products. For this purpose, scraping tools such as Beautiful Soup and Scrapy are used. The data is then analyzed to determine current market prices and price fluctuation trends.

[1068] 5. Notification of optimal timing

[1069] The server determines the optimal time to sell based on the acquired market value data and notifies the user. This notification is sent in real time via push notifications using Firebase Cloud Messaging.

[1070] 6. Auto-generated sales pitch

[1071] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the identified product's features and benefits. This description is an important factor in helping users sell their products at a higher price.

[1072] 7. Proposal of the best platform

[1073] The server proposes the most suitable sales platform for the user based on the acquired market value data and automatically generated product descriptions, allowing the user to easily find the best place to sell their products.

[1074] Specific examples

[1075] For example, consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone, uploads it to the app, and enters basic information such as when it was purchased and its condition. The device then sends this information to a server, which performs image analysis to identify the item. The server then researches market value from multiple online e-commerce platforms and notifies the user of the best time to sell. Furthermore, a generative AI model is used to automatically generate a sales description and suggests how to sell the item on the most suitable platform.

[1076] Examples of prompt statements

[1077] Examples of prompts include:

[1078] "Please generate a testimonial for a smartphone in good condition that you purchased in March 2021. Also, please suggest an effective way to sell it."

[1079] This allows users to easily maximize the value of their belongings and smoothly part with them in the right way.

[1080] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1081] Step 1:

[1082] The user uses their smartphone to take a photo of the item they want to get rid of and uploads it to the application. The input includes the product image and basic information (category, purchase date, condition). Based on this information, a data packet is generated to proceed to the next step.

[1083] Step 2:

[1084] The device (smartphone) combines the uploaded image and the entered basic information into a single data packet and sends it to the server via the Internet. The input is the product image and basic information data, and the output is a data packet sent to the server. This process uses the HTTPS protocol to ensure secure data transfer.

[1085] Step 3:

[1086] The server analyzes the received data packets and identifies the products using image analysis algorithms, such as TensorFlow or PyTorch, to extract features from the product images. The input is the image data and basic information, and the output is detailed information about the identified products (make, model, and other relevant information).

[1087] Step 4:

[1088] The server scrapes major e-commerce platforms to obtain the market value of the identified products. Tools such as Beautiful Soup and Scrapy are used to obtain price information published on the Internet. The input is product identification information, and the output is market value data (current market price, price fluctuation trend).

[1089] Step 5:

[1090] The server determines the optimal time to sell based on the acquired market value data. Here, an algorithm is used to analyze price fluctuation trends. The input is market value data, and the output is information on the optimal time to sell, which is notified to the user. This information is pushed in real time using Firebase Cloud Messaging.

[1091] Step 6:

[1092] The server uses a generative AI model (e.g., GPT-3) to automatically generate a sales description that reflects the features and benefits of the identified product. The input is product-specific information and basic information, and the output is the generated description. This description is then sent to the user's smartphone application.

[1093] Step 7:

[1094] The server recommends the most suitable sales platform for the user based on the acquired market value data and the automatically generated introduction. The input is the market value data and the generated introduction, and the output is information on the recommended sales platform. Based on this, the user can take specific steps to sell the product.

[1095] 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.

[1096] The present invention relates to a system that supports the appropriate management and disposal of unwanted items. The system includes a means for inputting belongings information, a means for identifying products based on the belongings information, a means for obtaining the market value of the identified products, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, and a means for supporting procedures for disposing of the products. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized notifications and advice.

[1097] Specific explanation of program processing

[1098] 1. The user takes a photo of an object and registers it

[1099] Users use their smartphone (device) camera to take a photo of the item they want to get rid of and upload it to a dedicated Danshari Agent app. Users also enter basic information about the item (category, purchase date, current condition, etc.). At this time, the emotion engine also works, analyzing the user's facial expressions and voice to recognize their current emotion.

[1100] 2. The device sends the image to the server

[1101] The device combines the image uploaded by the user, the basic information entered, and the recognized emotion information into a single data packet and transmits it to a server via the Internet.

[1102] 3. The server analyzes the image and identifies the product

[1103] The server analyzes the received image using an image analysis algorithm (e.g., computer vision technology). The server extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server uses the feature vector to identify the product based on a pre-trained machine learning model. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[1104] 4. The server retrieves detailed information about the identified product

[1105] The server retrieves detailed information about the identified item (make, model, specs, etc.) from the database. The server links the retrieved details to the inventory information.

[1106] 5. The server researches product value on the Internet

[1107] The server crawls major e-commerce platforms to obtain market values ​​related to the identified products. This allows it to collect and analyze data such as current market values, price fluctuation trends, and past transaction history. For example, the server collects the market value of "Manufacturer A's Model X Laptop" from mail-order and auction sites and calculates the average transaction price.

[1108] 6. The server notifies you based on the research results.

[1109] Based on the collected and analyzed data, the server uses an algorithm to determine the optimal time to sell, replace, or give away. Furthermore, the server adjusts the content and timing of notifications based on the user's emotions as recognized by the emotion engine. For example, if the server notifies the user that "the current market value is high, so now is the time to sell," but the emotion engine recognizes that the user appears busy, the server will delay the notification.

[1110] 7. The server provides follow-up on how to let go

[1111] The server suggests appropriate selling platforms and potential buyers. It also automatically generates sales descriptions and product descriptions for the product and provides them to the user. It also provides information on recycling and disposal methods to the user. For example, the server may provide support such as "automatically generating a sales description for a Model X laptop from manufacturer A to sell on online shopping site C, while also providing information on high-price transactions at second-hand shops."

[1112] Specific examples

[1113] Consider a case where a user wants to sell an old smartphone. The user takes a photo of the smartphone with their smartphone and uploads it to the app. The device then sends the photo, basic information, and the user's emotional state to the server. The server analyzes the image to identify the item and research its market value. It takes the user's emotional state into consideration when notifying them of when a high price can be expected. Finally, the server supports the sale on the platform selected by the user, generates a description, and provides potential buyers.

[1114] Through this system, users can maximize the value of their items without much effort and declutter efficiently and appropriately.The incorporation of an emotion engine enables flexible responses according to the user's situation, increasing user satisfaction.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The user activates the smartphone camera and takes a photo of the item they want to declutter. The emotion engine then captures the user's face with the camera and recognizes their emotion from their facial expressions and voice. After taking the photo, the user opens the dedicated Danshari Agent app, uploads the photo, and also enters basic information such as the item's category, purchase date, and condition.

[1118] Step 2:

[1119] The terminal combines the image uploaded by the user, the input basic information, and the user's emotional information (e.g., relaxed, stressed, harmful, etc.) recognized by the emotion engine into a single data packet, which is then sent to a server via the Internet.

[1120] Step 3:

[1121] The server analyzes the received image using an image analysis algorithm. It extracts features in the image (shape, color, brand logo, etc.) and generates a feature vector. The server then identifies the product based on a machine learning model trained using the feature vector. For example, the server might identify that "this image is of a Model X laptop from Manufacturer A."

[1122] Step 4:

[1123] The server retrieves detailed information about the identified item (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[1124] Step 5:

[1125] The server crawls major e-commerce platforms to obtain the market value of a specified product. It collects price data from mail-order and auction sites and analyzes the product's current market value, price fluctuation trends, and past transaction history. For example, the server might determine that "the average transaction price on mail-order site B over the past month was X yen."

[1126] Step 6:

[1127] Based on the data collected and analyzed by the server, an algorithm determines the optimal time to sell, replace, or give away the vehicle. At the same time, it also takes into account the user's emotional state and adjusts the content and timing of notifications. For example, if the server determines that "the current market value is high, so now is the time to sell," it will notify the user immediately if the user is relaxed, or at an appropriate time if the user is feeling stressed.

[1128] Step 7:

[1129] The server supports the process of disposing of the product. This includes suggesting suitable selling platforms, suggesting potential buyers, and automatically generating a sales description and product description. The server also provides users with information on recycling and disposal methods. For example, "Automatically generate a sales description for a Model X laptop from manufacturer A on online shopping site C, and also provide information on high-price offers at second-hand shops."

[1130] This process allows users to get maximum benefit with minimum effort, and the emotion engine delivers personalized responses.

[1131] Example 2

[1132] 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."

[1133] In today's world, managing and disposing of personal possessions is difficult, especially when it comes to finding the right way to dispose of items that are no longer in use. It's also difficult to understand the market value of items and dispose of them at the optimal time. Furthermore, there's a problem with low user satisfaction, as notifications and advice aren't provided that take the user's feelings into consideration.

[1134] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting belongings information, a means for identifying an object based on the belongings information, a means for acquiring the market value of the identified object, a means for monitoring fluctuations in the market value and notifying the user at an appropriate time, a means for supporting procedures for disposing of the object, and a means for recognizing the user's emotions and adjusting the content and timing of the notification. This allows the user to easily manage and dispose of items, and dispose of them at the optimal time while maximizing the value of the items. Furthermore, personalized services that take emotions into consideration are provided, which can improve user satisfaction.

[1135] "Belongings information" is information about items owned by the user, and specifically includes detailed information such as category, purchase date, and current condition.

[1136] The "object" refers to an item that the user wishes to dispose of or manage, and is identified based on a photographed image and input information.

[1137] "Market value" refers to the value of an item when it is currently traded in the market, and is determined based on data obtained from e-commerce platforms, etc.

[1138] An "emotion engine" refers to technology or software that analyzes a user's facial expressions and voice to recognize their current emotional state.

[1139] "Notification" refers to information sent from the server to the user, and includes information regarding fluctuations in market value and optimal disposal timing.

[1140] "Procedures" refers to the specific methods and procedures for disposing of the object, including support for actions such as selling, transferring, or discarding.

[1141] An "algorithm" refers to a set of calculation procedures or rules for solving a specific problem, and is used here to analyze market value and adjust notification timing.

[1142] This invention relates to a system that supports the proper management and disposal of unwanted items. This system works in conjunction with devices such as smartphones and tablets, and primarily assists in the input of belongings information, product identification, acquisition and monitoring of market value, timely notifications, and product disposal procedures. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized notifications and advice.

[1143] Hardware and software used

[1144] This system uses the following hardware and software:

[1145] Devices: smartphones, tablets

[1146] Server: Cloud server, database server

[1147] Software: Danshari agent app, image analysis algorithm, emotion engine, e-commerce platform crawling tool, generative AI model

[1148] Specific data processing and calculation

[1149] The user takes a photo of an object and registers the information.

[1150] Users use their device's camera to take a photo of the item they want to declutter and upload it to the dedicated Danshari Agent app. In addition to the photo, users enter basic information such as the category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice in real time to recognize their current emotional state.

[1151] The device sends the image and information to the server.

[1152] The device then combines the photo, basic information, and emotion information into a single data packet and transmits it to a server over the Internet using a secure communication protocol (e.g., HTTPS).

[1153] The server analyzes the image and identifies the product

[1154] The server analyzes the received image using an image analysis algorithm based on computer vision technology. The server extracts features such as shape, color, and brand logos from the image and generates a feature vector. This feature vector is then used to identify the relevant product based on a pre-trained machine learning model.

[1155] The server retrieves detailed information about the identified product.

[1156] The server retrieves detailed information about the identified product (such as manufacturer, model, and specifications) from the database and links it to the belongings information.

[1157] The server researches product values ​​on the Internet

[1158] The server crawls major e-commerce platforms (e.g., Amazon, eBay) to obtain the market value of the identified product. It then collects and analyzes data such as market value, price fluctuation trends, and past transaction history to calculate the current market value.

[1159] The server will notify you based on the research results.

[1160] The server uses an algorithm to determine the optimal time to sell, replace, or give away. It also adjusts the content and timing of notifications based on the user's emotions as determined by the emotion engine. For example, the server may notify users that "the current market value is high, so now is the time to sell," but if the emotion engine determines that the user is busy, it will delay the notification.

[1161] The server will provide follow-up on how to let go

[1162] The server suggests suitable selling platforms and potential buyers, and also uses a generative AI model to automatically generate sales descriptions and product descriptions, as well as provide users with information on recycling and disposal methods.

[1163] Specific examples

[1164] When a user has an old smartphone they want to get rid of, they take a photo of it and upload it to the app. The device then sends the photo, basic information, and the user's emotional state to a server. The server analyzes the image to identify the item and researches its market value, notifying users when a high price can be expected. The server then supports the sale on the user's platform of choice, generates a sales description, and provides potential buyers, allowing users to efficiently dispose of their items.

[1165] Prompt Sentence Examples

[1166] "Please describe your system for helping people dispose of their old smartphones. Explain in detail how the system retrieves information from users' smartphones, uses that information to determine market value, and recommends the best time and method for selling."

[1167] This allows users to maximize the value of their items without much effort and declutter efficiently and appropriately.The emotion engine enables flexible responses tailored to the user's situation, which is expected to increase user satisfaction.

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] The user takes a photo of the item they want to declutter and uploads it using the device app. As input, the user provides basic information such as the image file, category, purchase date, and current condition. The emotion engine analyzes the user's facial expressions and voice to generate current emotion information. As output, the device combines the photo, basic information, and emotion information into a single data packet.

[1171] Step 2:

[1172] The device sends data to the server. As input, the device receives a data packet that it has assembled. The device transmits the data packet to the server over the Internet using a secure communication protocol (e.g., HTTPS). The output of this transmission process is the data received by the server.

[1173] Step 3:

[1174] The server extracts image data from the received data packets and performs image analysis. As input, it uses the image file and basic information sent to the server. Using computer vision techniques, the server extracts features such as shape, color, and brand logos in the image and generates a feature vector. As output, it obtains the feature vector and analyzed data.

[1175] Step 4:

[1176] The server identifies the product using the feature vector. The feature vector obtained by image analysis is used as input. The server inputs the feature vector into a machine learning model to identify the product. The output is detailed information about the identified product (manufacturer, model, specifications, etc.).

[1177] Step 5:

[1178] The server retrieves the identified product details from the database. Using the identified product details as input, the server executes a database query to retrieve the relevant details. The output is the product details retrieved from the database.

[1179] Step 6:

[1180] The server collects market value information from the Internet. It uses detailed information about the identified product as input. The server crawls major e-commerce platforms to collect data such as the product's price, transaction history, and price fluctuation trends. The output is the collected market value data.

[1181] Step 7:

[1182] The server analyzes the market value data and notifies the user based on the results. The collected market value data and sentiment information are used as input. The server uses an algorithm to determine the optimal timing for selling and adjusts the content and timing of the notification taking into account the user's sentiment information. The output is the adjusted content and timing of the notification.

[1183] Step 8:

[1184] The server provides the user with support on how to sell the item. As input, it uses the previously identified item details and market value data. The server suggests suitable selling platforms and potential buyers, and uses a generative AI model to automatically generate a listing and description for the item. It also provides information on recycling and disposal methods. The output is an automatically generated listing, a suggested selling platform, and advice on how to dispose of the item.

[1185] (Application example 2)

[1186] 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."

[1187] In modern society, many people find it difficult to properly manage and part with unnecessary items. This problem stems from a lack of information to understand the market value of items and decide when to part with them. Furthermore, the lack of personalized advice based on the user's emotional state makes it difficult to make decisions about decluttering. There is a growing need for a system that provides efficient and appropriate support to address these challenges.

[1188] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting belongings information, means for identifying products based on the belongings information, means for acquiring the market value of the identified products, means for monitoring fluctuations in the market value and notifying the user at an appropriate time, means for supporting procedures for disposing of the products, means for recognizing the user's emotions and providing personalized notifications and advice, means for registering and managing products using a smartphone, means for identifying products using an image analysis algorithm, means for acquiring market values ​​from multiple e-commerce platforms on the Internet, and means for providing advice tailored to the user's situation using an emotion engine. This not only enables users to easily understand the market value of items and decide when to part with them, but also provides personalized advice tailored to the user's emotional state, thereby supporting the user's decision-making regarding decluttering.

[1189] - "Belongings Information" refers to detailed information about the items owned by the user, including the type of item, when it was purchased, its current condition, etc.

[1190] "Means for identifying the product" refers to a technical method for identifying the type of product an item is based on the entered belongings information.

[1191] "Means for obtaining market value" refers to a method for collecting the current market price or value of a specified commodity from data on the Internet.

[1192] "Means for monitoring fluctuations in market value and notifying users at the appropriate time" refers to technology that tracks trends in commodity market values ​​in real time and notifies users of the best time to sell or dispose of them.

[1193] "Means to support procedures for disposing of goods" refers to a system that provides users with information on how to choose a platform for selling goods, recycling methods, disposal procedures, etc.

[1194] "Means for recognizing a user's emotions and providing personalized notifications and advice" refers to technology that analyzes emotions from a user's facial expressions and voice, and uses that information to provide individually appropriate notifications and advice.

[1195] "Means for registering and managing products using a smartphone" refers to a method for entering and updating information about items owned by a user using a smartphone camera or application.

[1196] "Means for identifying products using image analysis algorithms" refers to technology that analyzes input image data and identifies the type of product the target item is based on the results.

[1197] "Means for obtaining market value from multiple e-commerce platforms on the Internet" refers to a method of collecting price information from multiple online marketplaces and calculating the market value of a product based on that data.

[1198] "Means for providing advice tailored to the user's situation using an emotion engine" refers to technology for providing advice with optimal timing and content based on the user's emotional state.

[1199] The present invention provides a system for supporting users in properly managing and letting go of their belongings, and is realized in the form of a smartphone application. The system includes the following means.

[1200] System Configuration

[1201] 1. How to enter your belongings:

[1202] Users take a photo of an item using their smartphone camera and upload it to the app, where they can enter basic information about the item (category, purchase date, current condition, etc.).

[1203] 2. Means of identifying the product:

[1204] The server uses image analysis algorithms to identify the item from the uploaded image, using computer vision techniques.

[1205] 3. Means of obtaining market value:

[1206] The server obtains the market value of the item from multiple e-commerce platforms on the Internet, and collects and analyzes market value data, price fluctuation trends, past transaction history, etc. related to the identified item.

[1207] 4. Means of monitoring market value fluctuations and notifying users in a timely manner:

[1208] The server uses an algorithm to determine the optimal time to sell based on the collected market value data and notifies the user. The content and timing of the notification also take into account the user's emotional state as recognized by an emotion engine.

[1209] 5. Supporting procedures for disposing of goods:

[1210] The server automatically generates and provides users with suitable selling platforms, potential buyers, and product descriptions for the items they are selling. It also provides information on recycling and disposal methods.

[1211] 6. How to recognize user emotions and provide personalized notifications and advice:

[1212] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and adjusts the content and timing of notifications based on the results.

[1213] Hardware and software used

[1214] Hardware:

[1215] Smartphone (camera, microphone, internet connection)

[1216] software:

[1217] Smartphone applications (e.g., Danshari Agent app)

[1218] Image analysis algorithms (e.g. OpenCV)

[1219] Machine learning models (e.g., scikit-learn)

[1220] Emotion Recognition Engine

[1221] Libraries for using data retrieval APIs (e.g., requests)

[1222] Specific examples

[1223] For example, if a user wants to get rid of an old smartphone, they can use the system as follows:

[1224] 1. The user takes a photo of their old smartphone and uploads it to the app. At the same time, they enter basic information about the item (category, purchase date, current condition, etc.). The prompt is: "I'd like to sell my old smartphone. I'll send you a photo so you can identify the item and tell me its market value. Also, please advise me on whether I should sell it now."

[1225] 2. The app sends the photo and basic information to a server, which uses image analysis algorithms to identify the item.

[1226] 3. The server obtains the market value of the identified item from multiple e-commerce platforms on the Internet and notifies the user of the optimal time to sell.

[1227] 4. An emotion engine analyzes the user's emotional state and provides personalized advice.

[1228] 5. Finally, we provide users with comprehensive support, including selecting a selling platform, automatically generating a sales description, and disposal methods such as recycling information.

[1229] In this way, the present invention allows users to maximize the value of their items efficiently and appropriately, making it easier for them to make decisions about decluttering.

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] Users take a photo of the item they want to get rid of with their smartphone and upload it to the app. They then enter basic information about the item, such as the category, purchase date, and current condition. The input data consists of a photo image file and basic text data.

[1233] Step 2:

[1234] The device combines the uploaded photo and basic information into a single data packet and sends it to the server. The input data is the image file and the basic information text data, and the output data is the data packet sent to the server.

[1235] Step 3:

[1236] The server analyzes the received images using an image analysis algorithm to identify the item. This process uses computer vision technology (e.g., OpenCV). The input data is the image file, and the output data is detailed information about the identified item (e.g., manufacturer, model, specifications, etc.).

[1237] Step 4:

[1238] The server retrieves detailed information of the identified item from the database and links it to the belongings information. The input data is the feature vector of the item, and the output data is detailed information of the item.

[1239] Step 5:

[1240] The server obtains market values ​​from multiple e-commerce platforms on the Internet. This process often uses web crawling technology. The input data is detailed information about the item, and the output data is market value data (e.g., current market price, price fluctuation trends, past transaction history, etc.).

[1241] Step 6:

[1242] The server analyzes the collected market value data and determines the optimal time to sell, replace, or give away the property based on an algorithm. The input data is the market value data, and the output data is the result of the decision on when to sell.

[1243] Step 7:

[1244] The server recognizes the user's emotions using an emotion engine and adjusts the content and timing of notifications. The input data is the user's facial expressions and voice data, and the output data is the result of emotion analysis.

[1245] Step 8:

[1246] The server takes into account the sentiment analysis results and sends appropriate notifications to the user. The notification content includes messages such as "Now is the time to sell because the current market value is high." The input data are the sentiment analysis results and market value data, and the output data is the notification message to the user.

[1247] Step 9:

[1248] The server provides after-sales support for disposing of items. Specifically, it selects a sales platform, automatically generates a description, and provides information on recycling and disposal methods. The input data is detailed information about the item and its market value, and the output data is after-sales support information for the user.

[1249] Through the above steps, the present invention can provide a system that enables users to maximize the value of items efficiently and appropriately and supports decision-making regarding decluttering.

[1250] 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.

[1251] 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.

[1252] 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 robot 414.

[1253] 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.

[1254] FIG. 9 illustrates 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 behaviors 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.

[1255] 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.

[1256] 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).

[1257] 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.

[1258] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1259] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1260] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1261] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1262] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1263] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1264] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1265] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1266] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1267] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1268] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1269] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1270] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1271] The following is further disclosed regarding the above embodiment.

[1272] (Claim 1)

[1273] A means for inputting belongings information;

[1274] A means for identifying a commodity based on the belongings information;

[1275] means for obtaining a market value of the identified commodity;

[1276] a means for monitoring fluctuations in the market value and notifying the user at an appropriate time;

[1277] means for supporting the process of disposing of said goods;

[1278] A system including:

[1279] (Claim 2)

[1280] 10. The system of claim 1, further comprising means for, if the belongings information is in the form of an image, analyzing the image to obtain detailed information about the identified item.

[1281] (Claim 3)

[1282] 2. The system of claim 1, wherein the market value is derived from data from multiple e-commerce platforms on the Internet.

[1283] "Example 1"

[1284] (Claim 1)

[1285] a means for a user to input belongings information;

[1286] means for transmitting the belongings information from the terminal to a server;

[1287] a means for identifying the product based on the belongings information by a server;

[1288] means for obtaining market values ​​of the identified products from a plurality of e-commerce platforms on the Internet;

[1289] a means for monitoring fluctuations in the market value and notifying the user at an appropriate time;

[1290] A means of supporting procedures and follow-up for disposing of the product;

[1291] A system including:

[1292] (Claim 2)

[1293] The system of claim 1 further comprising means for, if the belongings information is in image format, analyzing the image to extract product features and obtaining detailed information about the identified product using a generative AI model.

[1294] (Claim 3)

[1295] The system of claim 1 further comprises means for obtaining the market value based on data from multiple e-commerce platforms on the Internet, determining the analysis results using a generative AI model, and notifying the user.

[1296] "Application Example 1"

[1297] (Claim 1)

[1298] A means for inputting belongings information;

[1299] A means for identifying a commodity based on the belongings information;

[1300] means for obtaining a market value of the identified commodity;

[1301] a means for monitoring fluctuations in the market value and notifying the user at an appropriate time;

[1302] means for supporting the process of disposing of said goods;

[1303] a means for automatically generating sales descriptions using a generative AI model; and

[1304] means for suggesting the sale of the sales description on an optimal platform;

[1305] A system including:

[1306] (Claim 2)

[1307] 10. The system of claim 1, further comprising means for, if the belongings information is in the form of an image, analyzing the image to obtain detailed information about the identified item.

[1308] (Claim 3)

[1309] 2. The system of claim 1, wherein the market value is derived from data from multiple e-commerce platforms on the Internet.

[1310] "Example 2: Combining Emotion Engines"

[1311] (Claim 1)

[1312] A means for inputting belongings information;

[1313] A means for identifying an object based on the belongings information;

[1314] A means for acquiring a market value of the identified object;

[1315] a means for monitoring fluctuations in the market value and notifying the user at an appropriate time;

[1316] means for supporting a procedure for disposing of said object;

[1317] A means for recognizing a user's emotions and adjusting the content and timing of notifications;

[1318] A system including:

[1319] (Claim 2)

[1320] 10. The system of claim 1, further comprising means for, if the belongings information is in the form of an image, analyzing the image to obtain detailed information about the identified object.

[1321] (Claim 3)

[1322] 2. The system of claim 1, wherein the market value is derived from data from multiple e-commerce platforms on the Internet.

[1323] "Application example 2 when combining emotion engines"

[1324] (Claim 1)

[1325] A means for inputting belongings information;

[1326] A means for identifying a commodity based on the belongings information;

[1327] means for obtaining a market value of the identified commodity;

[1328] a means for monitoring fluctuations in the market value and notifying the user at an appropriate time;

[1329] means for supporting the process of disposing of said goods;

[1330] A means of recognizing user emotions and providing personalized notifications and advice;

[1331] A means to register and manage products using a smartphone;

[1332] means for identifying the product using an image analysis algorithm;

[1333] a means for obtaining market value from multiple e-commerce platforms on the Internet;

[1334] A means for providing advice according to a user's situation using an emotion engine;

[1335] A system including:

[1336] (Claim 2)

[1337] 10. The system of claim 1, further comprising means for, if the belongings information is in the form of an image, analyzing the image to obtain detailed information about the identified item.

[1338] (Claim 3)

[1339] The system of claim 1, wherein the market value is obtained based on data from multiple e-commerce platforms on the Internet, and further comprising means for incorporating an emotion engine to adjust the content and timing of notifications based on the user's emotions. [Explanation of symbols]

[1340] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting belongings information; A means for identifying a commodity based on the belongings information; means for obtaining a market value of the identified commodity; a means for monitoring fluctuations in the market value and notifying the user at an appropriate time; means for supporting the process of disposing of said goods; A system including:

2. 2. The system according to claim 1, further comprising means for, when the belongings information is in an image format, analyzing the image to obtain detailed information about the identified item.

3. 2. The system of claim 1, wherein the market value is derived from data from multiple e-commerce platforms on the Internet.

Images