system

Abstract

We provide the system. [Solution] A means for receiving image data captured by a user terminal, A means for analyzing received image data to evaluate the condition of an item, A method for calculating the predicted selling price of goods based on the evaluation results, A means of presenting the calculated selling price based on multiple sales channels, A means of obtaining and displaying information on donation fundraising organizations, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When efficiently disposing of items such as clothes and shoes that are no longer needed but in good condition, it takes a lot of time and effort to bring them to a recycling shop, and they are often discarded because the appraisal value is low. There is also a need to provide new options for users who feel pain in simply throwing them away and to promote social contribution.

Means for Solving the Problems

[0005] This invention provides a system that receives image data captured by a user terminal, analyzes the data, and evaluates the condition of an item. Based on the evaluated condition, it calculates and displays a predicted selling price of the item based on multiple sales channels. Furthermore, by acquiring and displaying information on charitable organizations, it enables users to select the optimal disposal method for the item, thereby promoting product reuse and social contribution.

[0006] A "user terminal" is a computer device operated by a user, capable of taking images and communicating.

[0007] "Image data" refers to data that represents the visual information of an object in digital format.

[0008] "Analysis" is the act of processing received data according to a specific process and understanding its meaning and content.

[0009] "Items" refer to clothing, shoes, and other items that users are looking to part with.

[0010] "Assessing the condition" means determining the condition of an item and identifying its usability and the extent of damage.

[0011] "Predicted selling price" is a price calculated based on the assumption that the goods will be traded in the market.

[0012] "Sales channels" refer to distribution routes and platforms used to bring goods to market, including flea market apps and second-hand shops.

[0013] A "donation fundraising organization" is an organization that accepts donations of goods or money and conducts activities aimed at contributing to society.

[0014] "Display" refers to the act of providing information to a user visually using the device's screen. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system that helps users efficiently evaluate unwanted items and select a disposal method. This system is implemented through a user terminal, a server, and a series of programs that work together via a network.

[0037] The user first takes pictures of the items they intend to sell or dispose of using their own mobile device or other user-operated device. These images are saved digitally and transmitted to the server via the network.

[0038] The server applies an image recognition algorithm to analyze the received image data. This algorithm is based on machine learning and automatically and accurately determines the condition, brand, model, and other characteristics of an item. Condition assessment analyzes the presence or absence of dirt or damage on the item's surface, signs of use, and general external features.

[0039] Next, the server uses the evaluated item's condition information to access past transaction data from flea market apps and recycling shops to calculate predicted selling prices across multiple sales channels. These prices are essentially estimates based on the item's condition and market demand.

[0040] Furthermore, the server retrieves information about relevant charitable organizations to enable users to choose to donate items. This information includes the organization's donation acceptance conditions and contact details.

[0041] Ultimately, the terminal displays the sales price and donation information sent from the server on the user's screen. Based on this information, the user can choose the appropriate way to dispose of the items. For example, if the predicted sales price is satisfactory, they can proceed with the sale; if the conditions are not suitable, they can choose to donate.

[0042] Through this series of processes, users can understand the value of the items and make the best choice. Therefore, the present invention can promote the reuse of items that would otherwise be discarded, contributing to waste reduction and social good.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user takes a picture of an item using the camera on their device. After taking the picture, the user reviews the image and chooses to upload it to the system.

[0046] Step 2:

[0047] The device formats the captured image data for transmission to the server. The image data is in a compressed format and prepared for secure transfer over the internet.

[0048] Step 3:

[0049] The server receives image data from the user's terminal. After receiving the data, the server applies an image recognition algorithm to evaluate the condition of the item. This process includes using deep learning to identify stains, damage, brand, and model of the item.

[0050] Step 4:

[0051] The server accesses past sales data from flea market apps and recycling shops based on the condition and identified characteristics of the items. It calculates and records the predicted selling price for each sales channel.

[0052] Step 5:

[0053] The server consults a database of organizations that accept donations and identifies recipients for the user's items. It then collects information on the recipient's acceptance criteria and contact details.

[0054] Step 6:

[0055] The server compiles the calculated estimated selling price and donation information and sends it to the user's terminal. This information is provided to the user to help them decide on a disposal method.

[0056] Step 7:

[0057] The terminal displays information received from the server to the user. The user can review the presented information and choose to sell or donate the items, or choose other options.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern society, disposing of unwanted items is a major challenge for individuals. In particular, accurately assessing the condition of items and determining the optimal selling price or disposal method is not easy. Furthermore, options such as effectively reusing items or donating them as a form of social contribution are complex, highlighting the need for a system that provides users with appropriate information quickly and efficiently.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving visual media acquired by a user's information device, means for analyzing the received visual media and automatically evaluating the condition of an item, and means for calculating a predicted market price of the item based on the evaluation result. This makes it possible to provide an appropriate method for valuing and disposing of an item.

[0063] "User information equipment" refers to electronic devices that can be operated by a user and are used to acquire images and information of objects and to communicate with a server.

[0064] "Visual media" refers to image and video data that visually represents the state and characteristics of an item, and is information acquired by user information devices.

[0065] A "server" is a computer system that analyzes information received via a network to evaluate the condition of goods and calculate their prices.

[0066] A "machine learning model" is a mathematical model used to learn from data and analyze or predict unknown information, and is applied to the evaluation of the condition of goods.

[0067] A "means of receiving" refers to a system that has the ability to receive visual media via a network and send it to a server for processing.

[0068] "Analysis" is the process of examining the content of a received visual medium and understanding its condition and characteristics.

[0069] "Means for automatically evaluating the state" refers to a function that provides a method for determining the state of an item by analyzing its characteristics using an algorithm.

[0070] A "means for calculating predicted market prices" is a system that has the ability to estimate the value of an item in the market based on its condition and past transaction data.

[0071] This invention is a system that efficiently evaluates unwanted items and supports appropriate disposal methods thereafter. The system is implemented using user information devices, a server, and a network infrastructure connecting them.

[0072] First, the user takes a picture of an item using a user information device such as a smartphone or PC. This device acquires the visual medium of the item and stores the image data locally. For example, a smartphone camera can be used to take a picture of a bag.

[0073] Next, the device sends the saved image data to the server via the network. This transmission can utilize Wi-Fi or mobile data communication, and data compression technology can be used. Once it is confirmed that the image has been successfully transmitted, the user device is notified.

[0074] The server runs a machine learning-based image recognition algorithm to analyze the received images. Deep learning frameworks such as TENSORFLOW® are used to automatically evaluate the condition, brand, and model of an item. For example, image recognition can determine if a bag is dirty or damaged.

[0075] Subsequently, the server calculates a predicted market price based on the item's condition assessment. This predicted market price is calculated using statistical methods and machine learning models, referencing a historical sales database and based on similar past transactions. In this process, it's possible to determine the market value of a specific brand of bag by referencing Amazon's transaction data.

[0076] The server also retrieves and provides information on appropriate charitable organizations for users who wish to donate goods. This includes each organization's donation acceptance conditions and contact information. By utilizing external APIs, the most up-to-date information is always available.

[0077] Ultimately, the terminal notifies and displays market price and donation information sent from the server to the user. The user can use this information to choose how to dispose of the item; for example, if they are satisfied with the displayed price, they can sell the bag, or if the conditions are not suitable, they can donate it to a local charity.

[0078] In this way, the system helps users receive an accurate valuation of their items and choose the most suitable disposal method. As a concrete example of using the generative AI model, the prompt can be entered as, "Upload a photo from my smartphone and tell me the selling price and donation information."

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user uses their smartphone to take pictures of the items they wish to sell or dispose of. The input consists of images of the items and involves taking photos using the camera. The output is the images being saved as digital data on the user's device.

[0082] Step 2:

[0083] The device transmits the captured image data to the server via the network. The input is the image file saved in step 1, and transmission is performed using various data communication protocols (e.g., HTTP, FTP). The output is the image data sent to the server.

[0084] Step 3:

[0085] The server applies an image recognition algorithm to analyze the received image data. The input is the image data sent to the server. Using machine learning frameworks such as TensorFlow, data processing is performed to extract image features and identify the state, brand, and model of the item. The output is the analyzed item state information.

[0086] Step 4:

[0087] The server calculates a predicted market price based on the item's condition information. The input is the item's condition information obtained in step 3. It uses a historical sales database and statistical methods and a generative AI model to predict the price. The output is the predicted market price of the item.

[0088] Step 5:

[0089] The server retrieves information on appropriate charities for users who wish to donate items. The input consists of search criteria based on the type and condition of the items. It uses an external API to retrieve current donation recipient information. The output is a list of relevant charitable organizations.

[0090] Step 6:

[0091] The terminal displays market price information and donation information sent from the server to the user. It receives information obtained from steps 4 and 5 as input. The information is presented clearly through the user interface. The output is a visual presentation of information to the user.

[0092] Step 7:

[0093] The user decides how to dispose of the items based on the information provided and enters the selected method into the terminal. The input consists of the selection of the disposal method and the instruction to execute it. The output is that the disposal of the items begins based on this selection.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In today's consumer society, there is a need to efficiently manage food waste generated daily in homes and businesses, reduce landfill disposal, and promote the effective use of food resources through methods such as reuse and donation. However, systems that appropriately assess the condition and expiration date of food and provide the optimal disposal method based on that assessment are still not fully developed.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for receiving data captured by a user terminal, means for analyzing the received data to evaluate the condition of the product, and means for estimating the expiration date of the product and determining whether it is reusable. This makes it possible to accurately evaluate the condition of food and automatically select and suggest the optimal disposal method.

[0099] A "user terminal" refers to a mobile device or computer device carried by a user, and is a device used for sending and receiving data.

[0100] "Means of receiving" refers to the methods and processes for acquiring data from external sources via a network.

[0101] "Means of analysis" refers to the technologies and algorithms used to process received data and understand or judge its content.

[0102] "Evaluating the condition of a product" refers to the act of determining the quality, degree of wear and tear, and usability of a product.

[0103] "Predicted price" refers to an estimated future selling value based on current information.

[0104] "Distribution channels" refer to the routes and media involved in the flow and process of how products and services reach consumers.

[0105] A "supplying organization" refers to an organization or institution that accepts donations of goods or services and operates with the aim of contributing to society.

[0106] "Means for estimating expiration dates and determining whether something is reusable" refers to methods for predicting the usable period of food or products and using that to determine whether recycling or resale is appropriate.

[0107] The system implementing this invention mainly consists of user terminals, a server, and a network connecting them. Mobile devices such as smartphones and tablets are used as user terminals. These terminals have the function of taking pictures of products such as food and transmitting the obtained images to the server in digital format.

[0108] The server uses machine learning models such as TensorFlow to analyze the received image data. Through image analysis, it is possible to automatically evaluate the expiration date and condition of products. Based on the analysis information, a server program using Node.js refers to a database stored in MongoDB and calculates a predicted price for the product based on its market value.

[0109] The server also retrieves the latest information from supplying organizations and informs users of the possibility of donating products. By clearly showing users the conditions and procedures for donation, users can quickly choose between reusing or donating.

[0110] As a concrete example, when a user takes yogurt nearing its expiration date out of the refrigerator and takes a picture of it with the app, the server receives and analyzes this photo. Based on the analysis, options such as selling it at a local flea market or donating it to a local food bank are presented. This entire process helps reduce food waste and promotes the efficient use of resources.

[0111] An example of a prompt message is shown as: "Develop an application that takes a picture of the ingredients left in your refrigerator and suggests the best way to reuse or sell them."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user takes a picture of the product using a device. Through this device operation, the captured image is converted into a digital format and prepared as input data for the system. This input data includes image information, including the product's appearance.

[0115] Step 2:

[0116] The device transmits the captured image data to the server via the network. The input here is the image data from the device, and the output is the same image data that reaches the server. The data is passed to the server through the transmission process.

[0117] Step 3:

[0118] The server applies an image recognition algorithm using TensorFlow to analyze the received image data. The input is image data, and the output is an evaluation value of the product's condition and an estimated expiration date. The data analysis determines the condition and degree of deterioration of the product.

[0119] Step 4:

[0120] The server queries a MongoDB database using a Node.js program based on the analysis, referencing historical product distribution data. The inputs are the product's condition rating and expiration date information, and the output is the predicted selling price. The predicted price is calculated based on market data.

[0121] Step 5:

[0122] The server determines whether an item is eligible for donation and retrieves information about the supplying organization. Inputs include the item's condition rating and expiration date, while outputs include donation conditions and detailed information about the supplying organization. Relevant conditions are considered to determine if an item is suitable for donation.

[0123] Step 6:

[0124] Finally, the server sends the calculated estimated selling price and information about the supplier to the terminal. The input is the result of the server's processing, and the output is the information that the user can view on the terminal. The information is displayed on the terminal screen, and the user can decide whether to reuse or donate the product.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system aimed at helping users effectively evaluate unwanted items and select appropriate disposal methods. This system is implemented through a user terminal, a server, an emotion engine, and a program connected via a network.

[0127] Users use their mobile devices to take pictures of items they are considering disposing of. Simultaneously, data such as the user's voice and facial expressions are collected during the photo-taking process. This creates a system that obtains information about the user's emotions.

[0128] The device sends captured image data and user sentiment data to the server. The server uses an image recognition algorithm to evaluate the condition of the item and predicts the selling price by referring to a past sales database based on the information obtained.

[0129] Furthermore, the server uses an emotion engine to analyze the user's emotions from the received voice and facial expression data. The analyzed emotional information is used to customize the suggested disposal methods to better reflect the user's feelings. For example, if the user feels an attachment to an item, the emotion engine can highlight the donation option.

[0130] The server sends the terminal an estimated selling price, sentiment-based disposal suggestions, and information on charitable organizations. This information is necessary for the user to decide on the appropriate disposal method for the item.

[0131] Ultimately, users can review the information displayed on their device and choose to sell, donate, or select other options. This system is expected to promote the reuse of items and increase social contribution by allowing users to make decisions that take their emotions into account when disposing of items.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user takes a picture of unwanted items using the camera on their device. Simultaneously, audio and facial expression data are collected through the device's microphone and camera. This also records the user's emotional response to the items.

[0135] Step 2:

[0136] The terminal processes the captured image data along with the user's voice and facial expression data, and sends it to the server. This data transmission takes place over the network, ensuring secure and efficient communication.

[0137] Step 3:

[0138] The server executes an image recognition algorithm to analyze the received image data. This process includes automatically determining the brand, model, and estimated condition of the item based on its usage.

[0139] Step 4:

[0140] The server calculates a predicted selling price based on the evaluation results of the items and by referring to a historical sales database. This process simultaneously simulates prices across multiple sales channels.

[0141] Step 5:

[0142] The server uses the user's voice and facial expression data to analyze it with an emotion engine and identify the user's emotions towards the item. Based on this data analysis, it formulates an emotion-specific disposal policy.

[0143] Step 6:

[0144] The server compiles information on the predicted selling price, sentiment-based recommended disposal methods, and relevant donation organizations, and sends it to the user's terminal.

[0145] Step 7:

[0146] The terminal displays information received from the server to the user. This display includes advice and suggestions for disposal methods tailored to the user's emotions.

[0147] Step 8:

[0148] Users can choose the appropriate disposal method for items based on the information displayed on their device. This allows for more emotionally responsive decision-making.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] Conventional item valuation systems could perform simple evaluations and calculate transaction prices based on image data of items, but they did not offer disposal methods that took user emotions into consideration. As a result, they only presented uniform disposal options without considering user feelings, which was a problem because it failed to alleviate the psychological burden on users.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for receiving digital data captured by an information processing device, means for analyzing the received digital data to evaluate the attributes of an item, and means for analyzing the user's emotional data using an emotion analysis device and customizing the disposal method based on the analysis results. This makes it possible to propose a disposal method that is sensitive to the user's emotions, in addition to an objective evaluation of the item.

[0154] An "information processing device" refers to an electronic device that has the function of acquiring, processing, and transmitting digital data.

[0155] "Digital data" refers to data that is represented in digital format, such as images, sounds, or other information, and is processed electronically.

[0156] "Attributes of an item" refer to the characteristics of the item itself, such as its type, appearance, condition, and function.

[0157] "Predicted transaction price" refers to the future market value of an item calculated based on past transaction data.

[0158] "Transaction intermediary" refers to a channel or platform for buying, selling, or trading goods.

[0159] An "emotion analysis device" refers to a system equipped with the ability to identify and analyze emotions from human voice, facial expressions, and behavior.

[0160] A "donation organization" refers to an organization that accepts donations for social purposes and uses them to carry out public service activities.

[0161] This invention is a system that allows users to evaluate and propose methods for disposing of unwanted items, and is implemented via an information processing device, a server, an emotion analysis device, and a network connecting these components.

[0162] The user takes an image of the item they intend to dispose of using their own information processing device. At the time of taking the image, the user can also provide audio and emotional information related to the item. This information forms the basis for the user's emotion analysis.

[0163] The device transmits image data and emotion data acquired from the user to the server. This process involves data transmission over a network such as the internet.

[0164] The server uses a combination of image recognition algorithms based on TensorFlow and natural language processing techniques suitable for sentiment analysis. This process evaluates the condition of items and calculates a predicted transaction price. Meanwhile, a sentiment analysis device grasps the user's emotions, and based on this, appropriate disposal methods such as selling, donating, or recycling are suggested to the user.

[0165] For example, in a case where a user is disposing of a camera they have used for many years, they can take a picture of the camera and input a voice message such as, "This camera holds many memories." Based on this prompt, the system will assess the camera's market value and present suggestions for donation destinations that resonate with the user's feelings.

[0166] This system combines objective evaluation of items with user sentiment to support appropriate and user-friendly disposal of goods.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user uses an information processing device to take images of items they are considering disposing of and inputs voice data. The input data consists of image data of the items and voice data indicating the user's emotions. This data is used for subsequent analysis.

[0170] Step 2:

[0171] The terminal transmits image and audio data obtained from the user to the server. The input consists of captured images and recorded audio, which are accurately transmitted to the server as output. This operation is performed using a secure communication protocol.

[0172] Step 3:

[0173] The server analyzes received image data using image recognition algorithms such as TensorFlow. The input is an image of an object; by analyzing the image, it identifies the object's attributes and generates output data about the object's condition and characteristics. Specifically, it determines the type of object and the extent of damage.

[0174] Step 4:

[0175] The server uses this attribute data to calculate a predicted transaction price by referencing past transaction data. The input is attribute data of the goods, and by matching it with the database, it predicts the market price and outputs an estimated market value. This calculation provides a concrete indicator of the selling price.

[0176] Step 5:

[0177] The server analyzes the received audio data using an emotion analysis device and evaluates the user's emotions. The input is the user's audio data, and natural language processing technology is used to analyze the emotional state and generate emotion information as output. This information is used to determine the user's current feelings.

[0178] Step 6:

[0179] The server presents the user with options to sell, donate, or recycle the item, based on its market value and the user's sentiment information. The input is the market value and sentiment information obtained in the previous step, and the program outputs customized options. This operation ensures that suggestions are tailored to the user's emotions.

[0180] Step 7:

[0181] The terminal displays information received from the server on the user's screen. The input is the proposed disposal options, which are visually presented as output on the user interface. The user makes a final decision based on the options presented here.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] When considering the disposal of goods, simply presenting a price without considering the user's feelings makes it difficult to make an appropriate disposal choice that takes into account their attachment to the goods and their emotional value. Conventional systems do not adequately support users in making the most satisfying choice based on their emotions. Therefore, the present invention aims to support disposal choices that take into account the emotional value of goods by providing evaluations of goods and presentations of disposal methods that take into account the user's emotional information.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for receiving image data captured by the user terminal and user emotion data; means for analyzing the received image data to evaluate the condition of the item and analyzing the user emotion data to obtain emotion information; and means for calculating the predicted selling price of the item based on the evaluation results and emotion information, and for suggesting a disposal method that is sensitive to the user's feelings. This makes it possible for the user to choose an appropriate disposal method for the item while also taking their own emotions into consideration.

[0187] A "user terminal" refers to a device, such as a mobile device or computer, used to acquire image data and emotional data of an item.

[0188] "Image data" refers to digital information that represents the appearance and characteristics of an object, expressed as an image taken by a user.

[0189] "Emotional data" refers to digital information that indicates a user's emotional state, obtained from their facial expressions, voice, and other sources.

[0190] A "server" is a central processing unit that receives data from user terminals via a network, performs evaluations of items and sentiment analysis, and returns the results.

[0191] "Evaluating the condition of an item" refers to the act of determining the quality and appearance of an item as captured using image recognition technology.

[0192] "Obtaining emotional information" is the process of analyzing a user's emotional data to determine their emotional state.

[0193] "Calculating a predicted selling price" is the act of calculating an expected selling price based on the results of an assessment of the condition of an item, and by referring to market data.

[0194] "Presenting disposal methods" means showing users options such as selling or donating items, and suggesting recommended methods that take emotional information into consideration.

[0195] In this embodiment of the invention, a user terminal, a cloud-based server, and software necessary for data processing work closely together to provide an item disposal support system that takes the user's emotions into consideration.

[0196] Users take pictures of unwanted items using their own devices, such as smartphones. The devices also have a mechanism to simultaneously capture emotional data, such as the user's voice and facial expressions. The user devices have applications installed for image capture, voice and facial expression analysis, and these data are collected in real time and sent to a cloud server.

[0197] The server analyzes the received image data using OpenCV and TensorFlow, and performs calculations using a machine learning model to evaluate the condition of the items. Next, it calculates a predicted selling price for the items by referring to historical market data. Furthermore, it processes emotional data using Google Cloud's sentiment analysis function to analyze the user's emotional state. Based on these analysis results, it determines whether the user has an attachment to the items and proposes customized and appropriate disposal methods, such as highlighting donation destination information or presenting sales options.

[0198] The results are sent back to the user's terminal from the server, allowing the user to review the information displayed on the screen and select the most suitable disposal method for the items. This process enables users to dispose of items while taking their own feelings and values ​​into consideration, leading to sustainable social contribution.

[0199] As a concrete example, if a user wants to dispose of an old camera, they take a picture of it with the app and send emotional data to the server. The server, recognizing that the user has a strong attachment to the camera, displays an estimated selling price and suggests possible donation options. This allows the user to choose the most suitable disposal method based on their emotions. An example of a prompt to input into the generating AI model would be, "What emotions do you feel about selling this item (e.g., an old camera)? What would be the ideal way to dispose of the item?"

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user takes pictures of the items they are considering disposing of using their user device. At this time, the device's camera and microphone are used to acquire image data of the items and the user's emotional data (voice and facial expression data). The input consists of images of the items, voice, and facial expression data, which are then prepared for transmission to the server.

[0203] Step 2:

[0204] The device sends the acquired image data and emotion data to a cloud server. It uses image data of objects and emotion data as input, and is configured to package and securely transmit the data. The cloud server holds the data in the format required for subsequent processing.

[0205] Step 3:

[0206] The server analyzes the received image data using image processing software (OpenCV). The input is image data of an item, which is used to recognize the item's features, and its condition is evaluated using an evaluation model (a constructed machine learning model). Based on the condition of the item output by this process, data is obtained to measure its value.

[0207] Step 4:

[0208] The server retrieves the item's condition assessment results and historical market data from a database, and uses this as input to calculate a predicted selling price. Data analysis using TensorFlow outputs the potential selling price of the item in the market.

[0209] Step 5:

[0210] The server processes voice and facial expression data using an emotion analysis engine. Using Google Cloud's emotion analysis API, it analyzes the emotion data as input and outputs user emotion information. This output is used to customize the disposal method, which will be proposed later.

[0211] Step 6:

[0212] The server integrates predicted selling price and sentiment information to suggest the disposal method (sale, donation, etc.) that best suits the user's psychological state. It uses predicted selling price and sentiment information as input, generates prompt sentences using a generative AI model, and outputs the optimal disposal suggestion.

[0213] Step 7:

[0214] The server sends the proposed details (predicted selling price, suggested disposal method) to the user's terminal. The output information includes highlighted recipient information and suggested sales options, which the user's terminal displays on its screen.

[0215] Step 8:

[0216] The user reviews the information provided through the terminal and makes a decision about disposing of the items based on their own feelings. After the user's selection, the entire system process is completed.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention provides a system that helps users efficiently evaluate unwanted items and select a disposal method. This system is implemented through a user terminal, a server, and a series of programs that work together via a network.

[0234] The user first takes pictures of the items they intend to sell or dispose of using their own mobile device or other user-operated device. These images are saved digitally and transmitted to the server via the network.

[0235] The server applies an image recognition algorithm to analyze the received image data. This algorithm is based on machine learning and automatically and accurately determines the condition, brand, model, and other characteristics of an item. Condition assessment analyzes the presence or absence of dirt or damage on the item's surface, signs of use, and general external features.

[0236] Next, the server uses the evaluated item's condition information to access past transaction data from flea market apps and recycling shops to calculate predicted selling prices across multiple sales channels. These prices are essentially estimates based on the item's condition and market demand.

[0237] Furthermore, the server retrieves information about relevant charitable organizations to enable users to choose to donate items. This information includes the organization's donation acceptance conditions and contact details.

[0238] Ultimately, the terminal displays the sales price and donation information sent from the server on the user's screen. Based on this information, the user can choose the appropriate way to dispose of the items. For example, if the predicted sales price is satisfactory, they can proceed with the sale; if the conditions are not suitable, they can choose to donate.

[0239] Through this series of processes, users can understand the value of the items and make the best choice. Therefore, the present invention can promote the reuse of items that would otherwise be discarded, contributing to waste reduction and social good.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user takes a picture of an item using the camera on their device. After taking the picture, the user reviews the image and chooses to upload it to the system.

[0243] Step 2:

[0244] The device formats the captured image data for transmission to the server. The image data is in a compressed format and prepared for secure transfer over the internet.

[0245] Step 3:

[0246] The server receives image data from the user's terminal. After receiving the data, the server applies an image recognition algorithm to evaluate the condition of the item. This process includes using deep learning to identify stains, damage, brand, and model of the item.

[0247] Step 4:

[0248] The server accesses past sales data from flea market apps and recycling shops based on the condition and identified characteristics of the items. It calculates and records the predicted selling price for each sales channel.

[0249] Step 5:

[0250] The server consults a database of organizations that accept donations and identifies recipients for the user's items. It then collects information on the recipient's acceptance criteria and contact details.

[0251] Step 6:

[0252] The server compiles the calculated estimated selling price and donation information and sends it to the user's terminal. This information is provided to the user to help them decide on a disposal method.

[0253] Step 7:

[0254] The terminal displays information received from the server to the user. The user can review the presented information and choose to sell or donate the items, or choose other options.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] In modern society, disposing of unwanted items is a major challenge for individuals. In particular, accurately assessing the condition of items and determining the optimal selling price or disposal method is not easy. Furthermore, options such as effectively reusing items or donating them as a form of social contribution are complex, highlighting the need for a system that provides users with appropriate information quickly and efficiently.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for receiving visual media acquired by a user's information device, means for analyzing the received visual media and automatically evaluating the condition of an item, and means for calculating a predicted market price of the item based on the evaluation result. This makes it possible to provide an appropriate method for valuing and disposing of an item.

[0260] "User information equipment" refers to electronic devices that can be operated by a user and are used to acquire images and information of objects and to communicate with a server.

[0261] "Visual media" refers to image and video data that visually represents the state and characteristics of an item, and is information acquired by user information devices.

[0262] A "server" is a computer system that analyzes information received via a network to evaluate the condition of goods and calculate their prices.

[0263] A "machine learning model" is a mathematical model used to learn from data and analyze or predict unknown information, and is applied to the evaluation of the condition of goods.

[0264] A "means of receiving" refers to a system that has the ability to receive visual media via a network and send it to a server for processing.

[0265] "Analysis" is the process of examining the content of a received visual medium and understanding its condition and characteristics.

[0266] "Means for automatically evaluating the state" refers to a function that provides a method for determining the state of an item by analyzing its characteristics using an algorithm.

[0267] A "means for calculating predicted market prices" is a system that has the ability to estimate the value of an item in the market based on its condition and past transaction data.

[0268] This invention is a system that efficiently evaluates unwanted items and supports appropriate disposal methods thereafter. The system is implemented using user information devices, a server, and a network infrastructure connecting them.

[0269] First, the user takes a picture of an item using a user information device such as a smartphone or PC. This device acquires the visual medium of the item and stores the image data locally. For example, a smartphone camera can be used to take a picture of a bag.

[0270] Next, the device sends the saved image data to the server via the network. This transmission can utilize Wi-Fi or mobile data communication, and data compression technology can be used. Once it is confirmed that the image has been successfully transmitted, the user device is notified.

[0271] The server runs a machine learning-based image recognition algorithm to analyze the received images. Deep learning frameworks such as TensorFlow are used to automatically evaluate the condition, brand, and model of the items. For example, image recognition can determine if a bag is dirty or damaged.

[0272] Subsequently, the server calculates a predicted market price based on the item's condition assessment. This predicted market price is calculated using statistical methods and machine learning models, referencing a historical sales database and based on similar past transactions. In this process, it's possible to determine the market value of a specific brand of bag by referencing Amazon's transaction data.

[0273] The server also retrieves and provides information on appropriate charitable organizations for users who wish to donate goods. This includes each organization's donation acceptance conditions and contact information. By utilizing external APIs, the most up-to-date information is always available.

[0274] Ultimately, the terminal notifies and displays market price and donation information sent from the server to the user. The user can use this information to choose how to dispose of the item; for example, if they are satisfied with the displayed price, they can sell the bag, or if the conditions are not suitable, they can donate it to a local charity.

[0275] In this way, the system helps users receive an accurate valuation of their items and choose the most suitable disposal method. As a concrete example of using the generative AI model, the prompt can be entered as, "Upload a photo from my smartphone and tell me the selling price and donation information."

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user uses a smartphone to take a picture of the item to be sold or disposed of. The input is an image of the item and includes the shooting operation using the camera. As output, the image is saved as digital data on the user's terminal.

[0279] Step 2:

[0280] The terminal sends the captured image data to the server via the network. The input is the image file saved in Step 1, and the transmission is performed using various data communication protocols (e.g., HTTP, FTP). As output, the image data is sent to the server.

[0281] Step 3:

[0282] The server applies an image recognition algorithm to analyze the received image data. The input is the image data sent to the server. Using a machine learning framework such as TensorFlow, the features of the image are extracted, and data processing is performed to identify the condition, brand, and model of the item. The output is the analyzed condition information of the item.

[0283] Step 4:

[0284] The server calculates the predicted market price based on the condition information of the item. The input is the condition information of the item obtained in Step 3. Referring to the past sales database, price prediction is performed using statistical methods and a generative AI model. The output is the predicted market price of the item.

[0285] Step 5:

[0286] The server obtains information on appropriate charities for users who wish to donate the item. The input is the search conditions based on the type and condition of the item. Using an external API, the current donation destination information is obtained. The output is a list of information on related donation organizations.

[0287] Step 6:

[0288] The terminal displays market price information and donation information sent from the server to the user. It receives information obtained from steps 4 and 5 as input. The information is presented clearly through the user interface. The output is a visual presentation of information to the user.

[0289] Step 7:

[0290] The user decides how to dispose of the items based on the information provided and enters the selected method into the terminal. The input consists of the selection of the disposal method and the instruction to execute it. The output is that the disposal of the items begins based on this selection.

[0291] (Application Example 1)

[0292] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0293] In today's consumer society, there is a need to efficiently manage food waste generated daily in homes and businesses, reduce landfill disposal, and promote the effective use of food resources through methods such as reuse and donation. However, systems that appropriately assess the condition and expiration date of food and provide the optimal disposal method based on that assessment are still not fully developed.

[0294] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0295] In this invention, the server includes means for receiving data captured by a user terminal, means for analyzing the received data to evaluate the condition of the product, and means for estimating the expiration date of the product and determining whether it is reusable. This makes it possible to accurately evaluate the condition of food and automatically select and suggest the optimal disposal method.

[0296] A "user terminal" refers to a mobile device or computer device carried by a user, and is a device used for sending and receiving data.

[0297] "Means of receiving" refers to the methods and processes for acquiring data from external sources via a network.

[0298] "Means of analysis" refers to the technologies and algorithms used to process received data and understand or judge its content.

[0299] "Evaluating the condition of a product" refers to the act of determining the quality, degree of wear and tear, and usability of a product.

[0300] "Predicted price" refers to an estimated future selling value based on current information.

[0301] "Distribution channels" refer to the routes and media involved in the flow and process of how products and services reach consumers.

[0302] A "supplying organization" refers to an organization or institution that accepts donations of goods or services and operates with the aim of contributing to society.

[0303] "Means for estimating expiration dates and determining whether something is reusable" refers to methods for predicting the usable period of food or products and using that to determine whether recycling or resale is appropriate.

[0304] The system implementing this invention mainly consists of user terminals, a server, and a network connecting them. Mobile devices such as smartphones and tablets are used as user terminals. These terminals have the function of taking pictures of products such as food and transmitting the obtained images to the server in digital format.

[0305] On the server, in order to analyze the received image data, a machine learning model such as TensorFlow is utilized. Through image analysis, it is possible to automatically evaluate the expiration date and condition of the product. Based on the analysis information, a server program using Node.js refers to the database stored in MongoDB and calculates the predicted price of the product based on the market value.

[0306] Also, the server obtains the latest information of the supply organization and indicates the possibility of donating the product to the user. By explicitly informing the user of the conditions and procedures for donation, the user can quickly select reuse or donation.

[0307] As a specific example, when a user takes out yogurt with an approaching expiration date from the refrigerator and takes a photo with the app, the server receives and analyzes this photo. Based on the analysis, the selling price in the local flea market and the donation option to the local food bank will be presented. Through this series of processes, food waste can be reduced and the effective utilization of resources can be promoted.

[0308] As an example of the prompt text, it is shown that it is input in the form of "Develop an application that takes pictures of the ingredients remaining in your refrigerator and proposes the optimal way to reuse or sell them."

[0309] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0310] Step 1:

[0311] The user takes a picture of the product using the terminal. Through the operation on this terminal, the taken picture is converted into a digital format and prepared as input data for the system. The input data is image information including the appearance of the product.

[0312] Step 2:

[0313] The device transmits the captured image data to the server via the network. The input here is the image data from the device, and the output is the same image data that reaches the server. The data is passed to the server through the transmission process.

[0314] Step 3:

[0315] The server applies an image recognition algorithm using TensorFlow to analyze the received image data. The input is image data, and the output is an evaluation value of the product's condition and an estimated expiration date. The data analysis determines the condition and degree of deterioration of the product.

[0316] Step 4:

[0317] The server queries a MongoDB database using a Node.js program based on the analysis, referencing historical product distribution data. The inputs are the product's condition rating and expiration date information, and the output is the predicted selling price. The predicted price is calculated based on market data.

[0318] Step 5:

[0319] The server determines whether an item is eligible for donation and retrieves information about the supplying organization. Inputs include the item's condition rating and expiration date, while outputs include donation conditions and detailed information about the supplying organization. Relevant conditions are considered to determine if an item is suitable for donation.

[0320] Step 6:

[0321] Finally, the server sends the calculated estimated selling price and information about the supplier to the terminal. The input is the result of the server's processing, and the output is the information that the user can view on the terminal. The information is displayed on the terminal screen, and the user can decide whether to reuse or donate the product.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] This invention is a system aimed at helping users effectively evaluate unwanted items and select appropriate disposal methods. This system is implemented through a user terminal, a server, an emotion engine, and a program connected via a network.

[0324] Users use their mobile devices to take pictures of items they are considering disposing of. Simultaneously, data such as the user's voice and facial expressions are collected during the photo-taking process. This creates a system that obtains information about the user's emotions.

[0325] The device sends captured image data and user sentiment data to the server. The server uses an image recognition algorithm to evaluate the condition of the item and predicts the selling price by referring to a past sales database based on the information obtained.

[0326] Furthermore, the server uses an emotion engine to analyze the user's emotions from the received voice and facial expression data. The analyzed emotional information is used to customize the suggested disposal methods to better reflect the user's feelings. For example, if the user feels an attachment to an item, the emotion engine can highlight the donation option.

[0327] The server sends the terminal an estimated selling price, sentiment-based disposal suggestions, and information on charitable organizations. This information is necessary for the user to decide on the appropriate disposal method for the item.

[0328] Ultimately, users can review the information displayed on their device and choose to sell, donate, or select other options. This system is expected to promote the reuse of items and increase social contribution by allowing users to make decisions that take their emotions into account when disposing of items.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user takes a picture of unwanted items using the camera on their device. Simultaneously, audio and facial expression data are collected through the device's microphone and camera. This also records the user's emotional response to the items.

[0332] Step 2:

[0333] The terminal processes the captured image data along with the user's voice and facial expression data, and sends it to the server. This data transmission takes place over the network, ensuring secure and efficient communication.

[0334] Step 3:

[0335] The server executes an image recognition algorithm to analyze the received image data. This process includes automatically determining the brand, model, and estimated condition of the item based on its usage.

[0336] Step 4:

[0337] The server calculates a predicted selling price based on the evaluation results of the items and by referring to a historical sales database. This process simultaneously simulates prices across multiple sales channels.

[0338] Step 5:

[0339] The server uses the user's voice and facial expression data to analyze it with an emotion engine and identify the user's emotions towards the item. Based on this data analysis, it formulates an emotion-specific disposal policy.

[0340] Step 6:

[0341] The server compiles information on the predicted selling price, sentiment-based recommended disposal methods, and relevant donation organizations, and sends it to the user's terminal.

[0342] Step 7:

[0343] The terminal displays information received from the server to the user. This display includes advice and suggestions for disposal methods tailored to the user's emotions.

[0344] Step 8:

[0345] Users can choose the appropriate disposal method for items based on the information displayed on their device. This allows for more emotionally responsive decision-making.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] Conventional item valuation systems could perform simple evaluations and calculate transaction prices based on image data of items, but they did not offer disposal methods that took user emotions into consideration. As a result, they only presented uniform disposal options without considering user feelings, which was a problem because it failed to alleviate the psychological burden on users.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for receiving digital data captured by an information processing device, means for analyzing the received digital data to evaluate the attributes of an item, and means for analyzing the user's emotional data using an emotion analysis device and customizing the disposal method based on the analysis results. This makes it possible to propose a disposal method that is sensitive to the user's emotions, in addition to an objective evaluation of the item.

[0351] An "information processing device" refers to an electronic device that has the function of acquiring, processing, and transmitting digital data.

[0352] "Digital data" refers to data that is represented in digital format, such as images, sounds, or other information, and is processed electronically.

[0353] "Attributes of an item" refer to the characteristics of the item itself, such as its type, appearance, condition, and function.

[0354] "Predicted transaction price" refers to the future market value of an item calculated based on past transaction data.

[0355] "Transaction intermediary" refers to a channel or platform for buying, selling, or trading goods.

[0356] An "emotion analysis device" refers to a system equipped with the ability to identify and analyze emotions from human voice, facial expressions, and behavior.

[0357] A "donation organization" refers to an organization that accepts donations for social purposes and uses them to carry out public service activities.

[0358] This invention is a system that allows users to evaluate and propose methods for disposing of unwanted items, and is implemented via an information processing device, a server, an emotion analysis device, and a network connecting these components.

[0359] The user takes an image of the item they intend to dispose of using their own information processing device. At the time of taking the image, the user can also provide audio and emotional information related to the item. This information forms the basis for the user's emotion analysis.

[0360] The device transmits image data and emotion data acquired from the user to the server. This process involves data transmission over a network such as the internet.

[0361] The server uses a combination of image recognition algorithms based on TensorFlow and natural language processing techniques suitable for sentiment analysis. This process evaluates the condition of items and calculates a predicted transaction price. Meanwhile, a sentiment analysis device grasps the user's emotions, and based on this, appropriate disposal methods such as selling, donating, or recycling are suggested to the user.

[0362] For example, in a case where a user is disposing of a camera they have used for many years, they can take a picture of the camera and input a voice message such as, "This camera holds many memories." Based on this prompt, the system will assess the camera's market value and present suggestions for donation destinations that resonate with the user's feelings.

[0363] This system combines objective evaluation of items with user sentiment to support appropriate and user-friendly disposal of goods.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user uses an information processing device to take images of items they are considering disposing of and inputs voice data. The input data consists of image data of the items and voice data indicating the user's emotions. This data is used for subsequent analysis.

[0367] Step 2:

[0368] The terminal transmits image and audio data obtained from the user to the server. The input consists of captured images and recorded audio, which are accurately transmitted to the server as output. This operation is performed using a secure communication protocol.

[0369] Step 3:

[0370] The server analyzes received image data using image recognition algorithms such as TensorFlow. The input is an image of an object; by analyzing the image, it identifies the object's attributes and generates output data about the object's condition and characteristics. Specifically, it determines the type of object and the extent of damage.

[0371] Step 4:

[0372] The server uses this attribute data to calculate a predicted transaction price by referencing past transaction data. The input is attribute data of the goods, and by matching it with the database, it predicts the market price and outputs an estimated market value. This calculation provides a concrete indicator of the selling price.

[0373] Step 5:

[0374] The server analyzes the received audio data using an emotion analysis device and evaluates the user's emotions. The input is the user's audio data, and natural language processing technology is used to analyze the emotional state and generate emotion information as output. This information is used to determine the user's current feelings.

[0375] Step 6:

[0376] The server presents the user with options to sell, donate, or recycle the item, based on its market value and the user's sentiment information. The input is the market value and sentiment information obtained in the previous step, and the program outputs customized options. This operation ensures that suggestions are tailored to the user's emotions.

[0377] Step 7:

[0378] The terminal displays information received from the server on the user's screen. The input is the proposed disposal options, which are visually presented as output on the user interface. The user makes a final decision based on the options presented here.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] When considering the disposal of goods, simply presenting a price without considering the user's feelings makes it difficult to make an appropriate disposal choice that takes into account their attachment to the goods and their emotional value. Conventional systems do not adequately support users in making the most satisfying choice based on their emotions. Therefore, the present invention aims to support disposal choices that take into account the emotional value of goods by providing evaluations of goods and presentations of disposal methods that take into account the user's emotional information.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for receiving image data captured by the user terminal and user emotion data; means for analyzing the received image data to evaluate the condition of the item and analyzing the user emotion data to obtain emotion information; and means for calculating the predicted selling price of the item based on the evaluation results and emotion information, and for suggesting a disposal method that is sensitive to the user's feelings. This makes it possible for the user to choose an appropriate disposal method for the item while also taking their own emotions into consideration.

[0384] A "user terminal" refers to a device, such as a mobile device or computer, used to acquire image data and emotional data of an item.

[0385] "Image data" refers to digital information that represents the appearance and characteristics of an object, expressed as an image taken by a user.

[0386] "Emotional data" refers to digital information that indicates a user's emotional state, obtained from their facial expressions, voice, and other sources.

[0387] A "server" is a central processing unit that receives data from user terminals via a network, performs evaluations of items and sentiment analysis, and returns the results.

[0388] "Evaluating the condition of an item" refers to the act of determining the quality and appearance of an item as captured using image recognition technology.

[0389] "Obtaining emotional information" is the process of analyzing a user's emotional data to determine their emotional state.

[0390] "Calculating a predicted selling price" is the act of calculating an expected selling price based on the results of an assessment of the condition of an item, and by referring to market data.

[0391] "Presenting disposal methods" means showing users options such as selling or donating items, and suggesting recommended methods that take emotional information into consideration.

[0392] In this embodiment of the invention, a user terminal, a cloud-based server, and software necessary for data processing work closely together to provide an item disposal support system that takes the user's emotions into consideration.

[0393] Users take pictures of unwanted items using their own devices, such as smartphones. The devices also have a mechanism to simultaneously capture emotional data, such as the user's voice and facial expressions. The user devices have applications installed for image capture, voice and facial expression analysis, and these data are collected in real time and sent to a cloud server.

[0394] The server analyzes the received image data using OpenCV and TensorFlow, and performs calculations using a machine learning model to evaluate the condition of the items. Next, it calculates a predicted selling price for the items by referring to historical market data. Furthermore, it processes emotional data using Google Cloud's sentiment analysis function to analyze the user's emotional state. Based on these analysis results, it determines whether the user has an attachment to the items and proposes customized and appropriate disposal methods, such as highlighting donation destination information or presenting sales options.

[0395] The results are sent back to the user's terminal from the server, allowing the user to review the information displayed on the screen and select the most suitable disposal method for the items. This process enables users to dispose of items while taking their own feelings and values ​​into consideration, leading to sustainable social contribution.

[0396] As a concrete example, if a user wants to dispose of an old camera, they take a picture of it with the app and send emotional data to the server. The server, recognizing that the user has a strong attachment to the camera, displays an estimated selling price and suggests possible donation options. This allows the user to choose the most suitable disposal method based on their emotions. An example of a prompt to input into the generating AI model would be, "What emotions do you feel about selling this item (e.g., an old camera)? What would be the ideal way to dispose of the item?"

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The user takes pictures of the items they are considering disposing of using their user device. At this time, the device's camera and microphone are used to acquire image data of the items and the user's emotional data (voice and facial expression data). The input consists of images of the items, voice, and facial expression data, which are then prepared for transmission to the server.

[0400] Step 2:

[0401] The device sends the acquired image data and emotion data to a cloud server. It uses image data of objects and emotion data as input, and is configured to package and securely transmit the data. The cloud server holds the data in the format required for subsequent processing.

[0402] Step 3:

[0403] The server analyzes the received image data using image processing software (OpenCV). The input is image data of an item, which is used to recognize the item's features, and its condition is evaluated using an evaluation model (a constructed machine learning model). Based on the condition of the item output by this process, data is obtained to measure its value.

[0404] Step 4:

[0405] The server retrieves the item's condition assessment results and historical market data from a database, and uses this as input to calculate a predicted selling price. Data analysis using TensorFlow outputs the potential selling price of the item in the market.

[0406] Step 5:

[0407] The server processes voice and facial expression data using an emotion analysis engine. Using Google Cloud's emotion analysis API, it analyzes the emotion data as input and outputs user emotion information. This output is used to customize the disposal method, which will be proposed later.

[0408] Step 6:

[0409] The server integrates predicted selling price and sentiment information to suggest the disposal method (sale, donation, etc.) that best suits the user's psychological state. It uses predicted selling price and sentiment information as input, generates prompt sentences using a generative AI model, and outputs the optimal disposal suggestion.

[0410] Step 7:

[0411] The server sends the proposed details (predicted selling price, suggested disposal method) to the user's terminal. The output information includes highlighted recipient information and suggested sales options, which the user's terminal displays on its screen.

[0412] Step 8:

[0413] The user reviews the information provided through the terminal and makes a decision about disposing of the items based on their own feelings. After the user's selection, the entire system process is completed.

[0414] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0415] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0420] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0422] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0424] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0425] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0426] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0427] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0429] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0430] This invention provides a system that helps users efficiently evaluate unwanted items and select a disposal method. This system is implemented through a user terminal, a server, and a series of programs that work together via a network.

[0431] The user first takes pictures of the items they intend to sell or dispose of using their own mobile device or other user-operated device. These images are saved digitally and transmitted to the server via the network.

[0432] The server applies an image recognition algorithm to analyze the received image data. This algorithm is based on machine learning and automatically and accurately determines the condition, brand, model, and other characteristics of an item. Condition assessment analyzes the presence or absence of dirt or damage on the item's surface, signs of use, and general external features.

[0433] Next, the server uses the evaluated item's condition information to access past transaction data from flea market apps and recycling shops to calculate predicted selling prices across multiple sales channels. These prices are essentially estimates based on the item's condition and market demand.

[0434] Furthermore, the server retrieves information about relevant charitable organizations to enable users to choose to donate items. This information includes the organization's donation acceptance conditions and contact details.

[0435] Ultimately, the terminal displays the sales price and donation information sent from the server on the user's screen. Based on this information, the user can choose the appropriate way to dispose of the items. For example, if the predicted sales price is satisfactory, they can proceed with the sale; if the conditions are not suitable, they can choose to donate.

[0436] Through this series of processes, users can understand the value of the items and make the best choice. Therefore, the present invention can promote the reuse of items that would otherwise be discarded, contributing to waste reduction and social good.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user takes a picture of an item using the camera on their device. After taking the picture, the user reviews the image and chooses to upload it to the system.

[0440] Step 2:

[0441] The device formats the captured image data for transmission to the server. The image data is in a compressed format and prepared for secure transfer over the internet.

[0442] Step 3:

[0443] The server receives image data from the user's terminal. After receiving the data, the server applies an image recognition algorithm to evaluate the condition of the item. This process includes using deep learning to identify stains, damage, brand, and model of the item.

[0444] Step 4:

[0445] The server accesses past sales data from flea market apps and recycling shops based on the condition and identified characteristics of the items. It calculates and records the predicted selling price for each sales channel.

[0446] Step 5:

[0447] The server consults a database of organizations that accept donations and identifies recipients for the user's items. It then collects information on the recipient's acceptance criteria and contact details.

[0448] Step 6:

[0449] The server compiles the calculated estimated selling price and donation information and sends it to the user's terminal. This information is provided to the user to help them decide on a disposal method.

[0450] Step 7:

[0451] The terminal displays information received from the server to the user. The user can review the presented information and choose to sell or donate the items, or choose other options.

[0452] (Example 1)

[0453] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] In modern society, disposing of unwanted items is a major challenge for individuals. In particular, accurately assessing the condition of items and determining the optimal selling price or disposal method is not easy. Furthermore, options such as effectively reusing items or donating them as a form of social contribution are complex, highlighting the need for a system that provides users with appropriate information quickly and efficiently.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes means for receiving visual media acquired by a user's information device, means for analyzing the received visual media and automatically evaluating the condition of an item, and means for calculating a predicted market price of the item based on the evaluation result. This makes it possible to provide an appropriate method for valuing and disposing of an item.

[0457] "User information equipment" refers to electronic devices that can be operated by a user and are used to acquire images and information of objects and to communicate with a server.

[0458] "Visual media" refers to image and video data that visually represents the state and characteristics of an item, and is information acquired by user information devices.

[0459] A "server" is a computer system that analyzes information received via a network to evaluate the condition of goods and calculate their prices.

[0460] A "machine learning model" is a mathematical model used to learn from data and analyze or predict unknown information, and is applied to the evaluation of the condition of goods.

[0461] A "means of receiving" refers to a system that has the ability to receive visual media via a network and send it to a server for processing.

[0462] "Analysis" is the process of examining the content of a received visual medium and understanding its condition and characteristics.

[0463] "Means for automatically evaluating the state" refers to a function that provides a method for determining the state of an item by analyzing its characteristics using an algorithm.

[0464] A "means for calculating predicted market prices" is a system that has the ability to estimate the value of an item in the market based on its condition and past transaction data.

[0465] This invention is a system that efficiently evaluates unwanted items and supports appropriate disposal methods thereafter. The system is implemented using user information devices, a server, and a network infrastructure connecting them.

[0466] First, the user takes a picture of an item using a user information device such as a smartphone or PC. This device acquires the visual medium of the item and stores the image data locally. For example, a smartphone camera can be used to take a picture of a bag.

[0467] Next, the device sends the saved image data to the server via the network. This transmission can utilize Wi-Fi or mobile data communication, and data compression technology can be used. Once it is confirmed that the image has been successfully transmitted, the user device is notified.

[0468] The server runs a machine learning-based image recognition algorithm to analyze the received images. Deep learning frameworks such as TensorFlow are used to automatically evaluate the condition, brand, and model of the items. For example, image recognition can determine if a bag is dirty or damaged.

[0469] Subsequently, the server calculates a predicted market price based on the item's condition assessment. This predicted market price is calculated using statistical methods and machine learning models, referencing a historical sales database and based on similar past transactions. In this process, it's possible to determine the market value of a specific brand of bag by referencing Amazon's transaction data.

[0470] The server also retrieves and provides information on appropriate charitable organizations for users who wish to donate goods. This includes each organization's donation acceptance conditions and contact information. By utilizing external APIs, the most up-to-date information is always available.

[0471] Ultimately, the terminal notifies and displays market price and donation information sent from the server to the user. The user can use this information to choose how to dispose of the item; for example, if they are satisfied with the displayed price, they can sell the bag, or if the conditions are not suitable, they can donate it to a local charity.

[0472] In this way, the system helps users receive an accurate valuation of their items and choose the most suitable disposal method. As a concrete example of using the generative AI model, the prompt can be entered as, "Upload a photo from my smartphone and tell me the selling price and donation information."

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The user uses their smartphone to take pictures of the items they wish to sell or dispose of. The input consists of images of the items and involves taking photos using the camera. The output is the images being saved as digital data on the user's device.

[0476] Step 2:

[0477] The device transmits the captured image data to the server via the network. The input is the image file saved in step 1, and transmission is performed using various data communication protocols (e.g., HTTP, FTP). The output is the image data sent to the server.

[0478] Step 3:

[0479] The server applies an image recognition algorithm to analyze the received image data. The input is the image data sent to the server. Using machine learning frameworks such as TensorFlow, data processing is performed to extract image features and identify the state, brand, and model of the item. The output is the analyzed item state information.

[0480] Step 4:

[0481] The server calculates a predicted market price based on the item's condition information. The input is the item's condition information obtained in step 3. It uses a historical sales database and statistical methods and a generative AI model to predict the price. The output is the predicted market price of the item.

[0482] Step 5:

[0483] The server retrieves information on appropriate charities for users who wish to donate items. The input consists of search criteria based on the type and condition of the items. It uses an external API to retrieve current donation recipient information. The output is a list of relevant charitable organizations.

[0484] Step 6:

[0485] The terminal displays market price information and donation information sent from the server to the user. It receives information obtained from steps 4 and 5 as input. The information is presented clearly through the user interface. The output is a visual presentation of information to the user.

[0486] Step 7:

[0487] The user decides how to dispose of the items based on the information provided and enters the selected method into the terminal. The input consists of the selection of the disposal method and the instruction to execute it. The output is that the disposal of the items begins based on this selection.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] In today's consumer society, there is a need to efficiently manage food waste generated daily in homes and businesses, reduce landfill disposal, and promote the effective use of food resources through methods such as reuse and donation. However, systems that appropriately assess the condition and expiration date of food and provide the optimal disposal method based on that assessment are still not fully developed.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for receiving data captured by a user terminal, means for analyzing the received data to evaluate the condition of the product, and means for estimating the expiration date of the product and determining whether it is reusable. This makes it possible to accurately evaluate the condition of food and automatically select and suggest the optimal disposal method.

[0493] A "user terminal" refers to a mobile device or computer device carried by a user, and is a device used for sending and receiving data.

[0494] "Means of receiving" refers to the methods and processes for acquiring data from external sources via a network.

[0495] "Means of analysis" refers to the technologies and algorithms used to process received data and understand or judge its content.

[0496] "Evaluating the condition of a product" refers to the act of determining the quality, degree of wear and tear, and usability of a product.

[0497] "Predicted price" refers to an estimated future selling value based on current information.

[0498] "Distribution channels" refer to the routes and media involved in the flow and process of how products and services reach consumers.

[0499] A "supplying organization" refers to an organization or institution that accepts donations of goods or services and operates with the aim of contributing to society.

[0500] "Means for estimating expiration dates and determining whether something is reusable" refers to methods for predicting the usable period of food or products and using that to determine whether recycling or resale is appropriate.

[0501] The system implementing this invention mainly consists of user terminals, a server, and a network connecting them. Mobile devices such as smartphones and tablets are used as user terminals. These terminals have the function of taking pictures of products such as food and transmitting the obtained images to the server in digital format.

[0502] The server uses machine learning models such as TensorFlow to analyze the received image data. Through image analysis, it is possible to automatically evaluate the expiration date and condition of products. Based on the analysis information, a server program using Node.js refers to a database stored in MongoDB and calculates a predicted price for the product based on its market value.

[0503] The server also retrieves the latest information from supplying organizations and informs users of the possibility of donating products. By clearly showing users the conditions and procedures for donation, users can quickly choose between reusing or donating.

[0504] As a concrete example, when a user takes yogurt nearing its expiration date out of the refrigerator and takes a picture of it with the app, the server receives and analyzes this photo. Based on the analysis, options such as selling it at a local flea market or donating it to a local food bank are presented. This entire process helps reduce food waste and promotes the efficient use of resources.

[0505] An example of a prompt message is shown as: "Develop an application that takes a picture of the ingredients left in your refrigerator and suggests the best way to reuse or sell them."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The user takes a picture of the product using a device. Through this device operation, the captured image is converted into a digital format and prepared as input data for the system. This input data includes image information, including the product's appearance.

[0509] Step 2:

[0510] The device transmits the captured image data to the server via the network. The input here is the image data from the device, and the output is the same image data that reaches the server. The data is passed to the server through the transmission process.

[0511] Step 3:

[0512] The server applies an image recognition algorithm using TensorFlow to analyze the received image data. The input is image data, and the output is an evaluation value of the product's condition and an estimated expiration date. The data analysis determines the condition and degree of deterioration of the product.

[0513] Step 4:

[0514] The server queries a MongoDB database using a Node.js program based on the analysis, referencing historical product distribution data. The inputs are the product's condition rating and expiration date information, and the output is the predicted selling price. The predicted price is calculated based on market data.

[0515] Step 5:

[0516] The server determines whether an item is eligible for donation and retrieves information about the supplying organization. Inputs include the item's condition rating and expiration date, while outputs include donation conditions and detailed information about the supplying organization. Relevant conditions are considered to determine if an item is suitable for donation.

[0517] Step 6:

[0518] Finally, the server sends the calculated estimated selling price and information about the supplier to the terminal. The input is the result of the server's processing, and the output is the information that the user can view on the terminal. The information is displayed on the terminal screen, and the user can decide whether to reuse or donate the product.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] This invention is a system aimed at helping users effectively evaluate unwanted items and select appropriate disposal methods. This system is implemented through a user terminal, a server, an emotion engine, and a program connected via a network.

[0521] Users use their mobile devices to take pictures of items they are considering disposing of. Simultaneously, data such as the user's voice and facial expressions are collected during the photo-taking process. This creates a system that obtains information about the user's emotions.

[0522] The device sends captured image data and user sentiment data to the server. The server uses an image recognition algorithm to evaluate the condition of the item and predicts the selling price by referring to a past sales database based on the information obtained.

[0523] Furthermore, the server uses an emotion engine to analyze the user's emotions from the received voice and facial expression data. The analyzed emotional information is used to customize the suggested disposal methods to better reflect the user's feelings. For example, if the user feels an attachment to an item, the emotion engine can highlight the donation option.

[0524] The server sends the terminal an estimated selling price, sentiment-based disposal suggestions, and information on charitable organizations. This information is necessary for the user to decide on the appropriate disposal method for the item.

[0525] Ultimately, users can review the information displayed on their device and choose to sell, donate, or select other options. This system is expected to promote the reuse of items and increase social contribution by allowing users to make decisions that take their emotions into account when disposing of items.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user takes a picture of unwanted items using the camera on their device. Simultaneously, audio and facial expression data are collected through the device's microphone and camera. This also records the user's emotional response to the items.

[0529] Step 2:

[0530] The terminal processes the captured image data along with the user's voice and facial expression data, and sends it to the server. This data transmission takes place over the network, ensuring secure and efficient communication.

[0531] Step 3:

[0532] The server executes an image recognition algorithm to analyze the received image data. This process includes automatically determining the brand, model, and estimated condition of the item based on its usage.

[0533] Step 4:

[0534] The server calculates a predicted selling price based on the evaluation results of the items and by referring to a historical sales database. This process simultaneously simulates prices across multiple sales channels.

[0535] Step 5:

[0536] The server uses the user's voice and facial expression data to analyze it with an emotion engine and identify the user's emotions towards the item. Based on this data analysis, it formulates an emotion-specific disposal policy.

[0537] Step 6:

[0538] The server compiles information on the predicted selling price, sentiment-based recommended disposal methods, and relevant donation organizations, and sends it to the user's terminal.

[0539] Step 7:

[0540] The terminal displays information received from the server to the user. This display includes advice and suggestions for disposal methods tailored to the user's emotions.

[0541] Step 8:

[0542] Users can choose the appropriate disposal method for items based on the information displayed on their device. This allows for more emotionally responsive decision-making.

[0543] (Example 2)

[0544] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] Conventional item valuation systems could perform simple evaluations and calculate transaction prices based on image data of items, but they did not offer disposal methods that took user emotions into consideration. As a result, they only presented uniform disposal options without considering user feelings, which was a problem because it failed to alleviate the psychological burden on users.

[0546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0547] In this invention, the server includes means for receiving digital data captured by an information processing device, means for analyzing the received digital data to evaluate the attributes of an item, and means for analyzing the user's emotional data using an emotion analysis device and customizing the disposal method based on the analysis results. This makes it possible to propose a disposal method that is sensitive to the user's emotions, in addition to an objective evaluation of the item.

[0548] An "information processing device" refers to an electronic device that has the function of acquiring, processing, and transmitting digital data.

[0549] "Digital data" refers to data that is represented in digital format, such as images, sounds, or other information, and is processed electronically.

[0550] "Attributes of an item" refer to the characteristics of the item itself, such as its type, appearance, condition, and function.

[0551] "Predicted transaction price" refers to the future market value of an item calculated based on past transaction data.

[0552] "Transaction intermediary" refers to a channel or platform for buying, selling, or trading goods.

[0553] An "emotion analysis device" refers to a system equipped with the ability to identify and analyze emotions from human voice, facial expressions, and behavior.

[0554] A "donation organization" refers to an organization that accepts donations for social purposes and uses them to carry out public service activities.

[0555] This invention is a system that allows users to evaluate and propose methods for disposing of unwanted items, and is implemented via an information processing device, a server, an emotion analysis device, and a network connecting these components.

[0556] The user takes an image of the item they intend to dispose of using their own information processing device. At the time of taking the image, the user can also provide audio and emotional information related to the item. This information forms the basis for the user's emotion analysis.

[0557] The device transmits image data and emotion data acquired from the user to the server. This process involves data transmission over a network such as the internet.

[0558] The server uses a combination of image recognition algorithms based on TensorFlow and natural language processing techniques suitable for sentiment analysis. This process evaluates the condition of items and calculates a predicted transaction price. Meanwhile, a sentiment analysis device grasps the user's emotions, and based on this, appropriate disposal methods such as selling, donating, or recycling are suggested to the user.

[0559] For example, in a case where a user is disposing of a camera they have used for many years, they can take a picture of the camera and input a voice message such as, "This camera holds many memories." Based on this prompt, the system will assess the camera's market value and present suggestions for donation destinations that resonate with the user's feelings.

[0560] This system combines objective evaluation of items with user sentiment to support appropriate and user-friendly disposal of goods.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The user uses an information processing device to take images of items they are considering disposing of and inputs voice data. The input data consists of image data of the items and voice data indicating the user's emotions. This data is used for subsequent analysis.

[0564] Step 2:

[0565] The terminal transmits image and audio data obtained from the user to the server. The input consists of captured images and recorded audio, which are accurately transmitted to the server as output. This operation is performed using a secure communication protocol.

[0566] Step 3:

[0567] The server analyzes received image data using image recognition algorithms such as TensorFlow. The input is an image of an object; by analyzing the image, it identifies the object's attributes and generates output data about the object's condition and characteristics. Specifically, it determines the type of object and the extent of damage.

[0568] Step 4:

[0569] The server uses this attribute data to calculate a predicted transaction price by referencing past transaction data. The input is attribute data of the goods, and by matching it with the database, it predicts the market price and outputs an estimated market value. This calculation provides a concrete indicator of the selling price.

[0570] Step 5:

[0571] The server analyzes the received audio data using an emotion analysis device and evaluates the user's emotions. The input is the user's audio data, and natural language processing technology is used to analyze the emotional state and generate emotion information as output. This information is used to determine the user's current feelings.

[0572] Step 6:

[0573] The server presents the user with options to sell, donate, or recycle the item, based on its market value and the user's sentiment information. The input is the market value and sentiment information obtained in the previous step, and the program outputs customized options. This operation ensures that suggestions are tailored to the user's emotions.

[0574] Step 7:

[0575] The terminal displays information received from the server on the user's screen. The input is the proposed disposal options, which are visually presented as output on the user interface. The user makes a final decision based on the options presented here.

[0576] (Application Example 2)

[0577] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] When considering the disposal of goods, simply presenting a price without considering the user's feelings makes it difficult to make an appropriate disposal choice that takes into account their attachment to the goods and their emotional value. Conventional systems do not adequately support users in making the most satisfying choice based on their emotions. Therefore, the present invention aims to support disposal choices that take into account the emotional value of goods by providing evaluations of goods and presentations of disposal methods that take into account the user's emotional information.

[0579] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0580] In this invention, the server includes means for receiving image data captured by the user terminal and user emotion data; means for analyzing the received image data to evaluate the condition of the item and analyzing the user emotion data to obtain emotion information; and means for calculating the predicted selling price of the item based on the evaluation results and emotion information, and for suggesting a disposal method that is sensitive to the user's feelings. This makes it possible for the user to choose an appropriate disposal method for the item while also taking their own emotions into consideration.

[0581] A "user terminal" refers to a device, such as a mobile device or computer, used to acquire image data and emotional data of an item.

[0582] "Image data" refers to digital information that represents the appearance and characteristics of an object, expressed as an image taken by a user.

[0583] "Emotional data" refers to digital information that indicates a user's emotional state, obtained from their facial expressions, voice, and other sources.

[0584] A "server" is a central processing unit that receives data from user terminals via a network, performs evaluations of items and sentiment analysis, and returns the results.

[0585] "Evaluating the condition of an item" refers to the act of determining the quality and appearance of an item as captured using image recognition technology.

[0586] "Obtaining emotional information" is the process of analyzing a user's emotional data to determine their emotional state.

[0587] "Calculating a predicted selling price" is the act of calculating an expected selling price based on the results of an assessment of the condition of an item, and by referring to market data.

[0588] "Presenting disposal methods" means showing users options such as selling or donating items, and suggesting recommended methods that take emotional information into consideration.

[0589] In this embodiment of the invention, a user terminal, a cloud-based server, and software necessary for data processing work closely together to provide an item disposal support system that takes the user's emotions into consideration.

[0590] Users take pictures of unwanted items using their own devices, such as smartphones. The devices also have a mechanism to simultaneously capture emotional data, such as the user's voice and facial expressions. The user devices have applications installed for image capture, voice and facial expression analysis, and these data are collected in real time and sent to a cloud server.

[0591] The server analyzes the received image data using OpenCV and TensorFlow, and performs calculations using a machine learning model to evaluate the condition of the items. Next, it calculates a predicted selling price for the items by referring to historical market data. Furthermore, it processes emotional data using Google Cloud's sentiment analysis function to analyze the user's emotional state. Based on these analysis results, it determines whether the user has an attachment to the items and proposes customized and appropriate disposal methods, such as highlighting donation destination information or presenting sales options.

[0592] The results are sent back to the user's terminal from the server, allowing the user to review the information displayed on the screen and select the most suitable disposal method for the items. This process enables users to dispose of items while taking their own feelings and values ​​into consideration, leading to sustainable social contribution.

[0593] As a concrete example, if a user wants to dispose of an old camera, they take a picture of it with the app and send emotional data to the server. The server, recognizing that the user has a strong attachment to the camera, displays an estimated selling price and suggests possible donation options. This allows the user to choose the most suitable disposal method based on their emotions. An example of a prompt to input into the generating AI model would be, "What emotions do you feel about selling this item (e.g., an old camera)? What would be the ideal way to dispose of the item?"

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The user takes pictures of the items they are considering disposing of using their user device. At this time, the device's camera and microphone are used to acquire image data of the items and the user's emotional data (voice and facial expression data). The input consists of images of the items, voice, and facial expression data, which are then prepared for transmission to the server.

[0597] Step 2:

[0598] The device sends the acquired image data and emotion data to a cloud server. It uses image data of objects and emotion data as input, and is configured to package and securely transmit the data. The cloud server holds the data in the format required for subsequent processing.

[0599] Step 3:

[0600] The server analyzes the received image data using image processing software (OpenCV). The input is image data of an item, which is used to recognize the item's features, and its condition is evaluated using an evaluation model (a constructed machine learning model). Based on the condition of the item output by this process, data is obtained to measure its value.

[0601] Step 4:

[0602] The server retrieves the item's condition assessment results and historical market data from a database, and uses this as input to calculate a predicted selling price. Data analysis using TensorFlow outputs the potential selling price of the item in the market.

[0603] Step 5:

[0604] The server processes voice and facial expression data using an emotion analysis engine. Using Google Cloud's emotion analysis API, it analyzes the emotion data as input and outputs user emotion information. This output is used to customize the disposal method, which will be proposed later.

[0605] Step 6:

[0606] The server integrates predicted selling price and sentiment information to suggest the disposal method (sale, donation, etc.) that best suits the user's psychological state. It uses predicted selling price and sentiment information as input, generates prompt sentences using a generative AI model, and outputs the optimal disposal suggestion.

[0607] Step 7:

[0608] The server sends the proposed details (predicted selling price, suggested disposal method) to the user's terminal. The output information includes highlighted recipient information and suggested sales options, which the user's terminal displays on its screen.

[0609] Step 8:

[0610] The user reviews the information provided through the terminal and makes a decision about disposing of the items based on their own feelings. After the user's selection, the entire system process is completed.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] This invention provides a system that helps users efficiently evaluate unwanted items and select a disposal method. This system is implemented through a user terminal, a server, and a series of programs that work together via a network.

[0629] The user first takes pictures of the items they intend to sell or dispose of using their own mobile device or other user-operated device. These images are saved digitally and transmitted to the server via the network.

[0630] The server applies an image recognition algorithm to analyze the received image data. This algorithm is based on machine learning and automatically and accurately determines the condition, brand, model, and other characteristics of an item. Condition assessment analyzes the presence or absence of dirt or damage on the item's surface, signs of use, and general external features.

[0631] Next, the server uses the evaluated item's condition information to access past transaction data from flea market apps and recycling shops to calculate predicted selling prices across multiple sales channels. These prices are essentially estimates based on the item's condition and market demand.

[0632] Furthermore, the server retrieves information about relevant charitable organizations to enable users to choose to donate items. This information includes the organization's donation acceptance conditions and contact details.

[0633] Ultimately, the terminal displays the sales price and donation information sent from the server on the user's screen. Based on this information, the user can choose the appropriate way to dispose of the items. For example, if the predicted sales price is satisfactory, they can proceed with the sale; if the conditions are not suitable, they can choose to donate.

[0634] Through this series of processes, users can understand the value of the items and make the best choice. Therefore, the present invention can promote the reuse of items that would otherwise be discarded, contributing to waste reduction and social good.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user takes a picture of an item using the camera on their device. After taking the picture, the user reviews the image and chooses to upload it to the system.

[0638] Step 2:

[0639] The device formats the captured image data for transmission to the server. The image data is in a compressed format and prepared for secure transfer over the internet.

[0640] Step 3:

[0641] The server receives image data from the user's terminal. After receiving the data, the server applies an image recognition algorithm to evaluate the condition of the item. This process includes using deep learning to identify stains, damage, brand, and model of the item.

[0642] Step 4:

[0643] The server accesses past sales data from flea market apps and recycling shops based on the condition and identified characteristics of the items. It calculates and records the predicted selling price for each sales channel.

[0644] Step 5:

[0645] The server consults a database of organizations that accept donations and identifies recipients for the user's items. It then collects information on the recipient's acceptance criteria and contact details.

[0646] Step 6:

[0647] The server compiles the calculated estimated selling price and donation information and sends it to the user's terminal. This information is provided to the user to help them decide on a disposal method.

[0648] Step 7:

[0649] The terminal displays information received from the server to the user. The user can review the presented information and choose to sell or donate the items, or choose other options.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] In modern society, disposing of unwanted items is a major challenge for individuals. In particular, accurately assessing the condition of items and determining the optimal selling price or disposal method is not easy. Furthermore, options such as effectively reusing items or donating them as a form of social contribution are complex, highlighting the need for a system that provides users with appropriate information quickly and efficiently.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] In this invention, the server includes means for receiving visual media acquired by a user's information device, means for analyzing the received visual media and automatically evaluating the condition of an item, and means for calculating a predicted market price of the item based on the evaluation result. This makes it possible to provide an appropriate method for valuing and disposing of an item.

[0655] "User information equipment" refers to electronic devices that can be operated by a user and are used to acquire images and information of objects and to communicate with a server.

[0656] "Visual media" refers to image and video data that visually represents the state and characteristics of an item, and is information acquired by user information devices.

[0657] A "server" is a computer system that analyzes information received via a network to evaluate the condition of goods and calculate their prices.

[0658] A "machine learning model" is a mathematical model used to learn from data and analyze or predict unknown information, and is applied to the evaluation of the condition of goods.

[0659] A "means of receiving" refers to a system that has the ability to receive visual media via a network and send it to a server for processing.

[0660] "Analysis" is the process of examining the content of a received visual medium and understanding its condition and characteristics.

[0661] "Means for automatically evaluating the state" refers to a function that provides a method for determining the state of an item by analyzing its characteristics using an algorithm.

[0662] A "means for calculating predicted market prices" is a system that has the ability to estimate the value of an item in the market based on its condition and past transaction data.

[0663] This invention is a system that efficiently evaluates unwanted items and supports appropriate disposal methods thereafter. The system is implemented using user information devices, a server, and a network infrastructure connecting them.

[0664] First, the user takes a picture of an item using a user information device such as a smartphone or PC. This device acquires the visual medium of the item and stores the image data locally. For example, a smartphone camera can be used to take a picture of a bag.

[0665] Next, the device sends the saved image data to the server via the network. This transmission can utilize Wi-Fi or mobile data communication, and data compression technology can be used. Once it is confirmed that the image has been successfully transmitted, the user device is notified.

[0666] The server runs a machine learning-based image recognition algorithm to analyze the received images. Deep learning frameworks such as TensorFlow are used to automatically evaluate the condition, brand, and model of the items. For example, image recognition can determine if a bag is dirty or damaged.

[0667] Subsequently, the server calculates a predicted market price based on the item's condition assessment. This predicted market price is calculated using statistical methods and machine learning models, referencing a historical sales database and based on similar past transactions. In this process, it's possible to determine the market value of a specific brand of bag by referencing Amazon's transaction data.

[0668] The server also retrieves and provides information on appropriate charitable organizations for users who wish to donate goods. This includes each organization's donation acceptance conditions and contact information. By utilizing external APIs, the most up-to-date information is always available.

[0669] Ultimately, the terminal notifies and displays market price and donation information sent from the server to the user. The user can use this information to choose how to dispose of the item; for example, if they are satisfied with the displayed price, they can sell the bag, or if the conditions are not suitable, they can donate it to a local charity.

[0670] In this way, the system helps users receive an accurate valuation of their items and choose the most suitable disposal method. As a concrete example of using the generative AI model, the prompt can be entered as, "Upload a photo from my smartphone and tell me the selling price and donation information."

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The user uses their smartphone to take pictures of the items they wish to sell or dispose of. The input consists of images of the items and involves taking photos using the camera. The output is the images being saved as digital data on the user's device.

[0674] Step 2:

[0675] The device transmits the captured image data to the server via the network. The input is the image file saved in step 1, and transmission is performed using various data communication protocols (e.g., HTTP, FTP). The output is the image data sent to the server.

[0676] Step 3:

[0677] The server applies an image recognition algorithm to analyze the received image data. The input is the image data sent to the server. Using machine learning frameworks such as TensorFlow, data processing is performed to extract image features and identify the state, brand, and model of the item. The output is the analyzed item state information.

[0678] Step 4:

[0679] The server calculates a predicted market price based on the item's condition information. The input is the item's condition information obtained in step 3. It uses a historical sales database and statistical methods and a generative AI model to predict the price. The output is the predicted market price of the item.

[0680] Step 5:

[0681] The server retrieves information on appropriate charities for users who wish to donate items. The input consists of search criteria based on the type and condition of the items. It uses an external API to retrieve current donation recipient information. The output is a list of relevant charitable organizations.

[0682] Step 6:

[0683] The terminal displays market price information and donation information sent from the server to the user. It receives information obtained from steps 4 and 5 as input. The information is presented clearly through the user interface. The output is a visual presentation of information to the user.

[0684] Step 7:

[0685] The user decides how to dispose of the items based on the information provided and enters the selected method into the terminal. The input consists of the selection of the disposal method and the instruction to execute it. The output is that the disposal of the items begins based on this selection.

[0686] (Application Example 1)

[0687] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In today's consumer society, there is a need to efficiently manage food waste generated daily in homes and businesses, reduce landfill disposal, and promote the effective use of food resources through methods such as reuse and donation. However, systems that appropriately assess the condition and expiration date of food and provide the optimal disposal method based on that assessment are still not fully developed.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0690] In this invention, the server includes means for receiving data captured by a user terminal, means for analyzing the received data to evaluate the condition of the product, and means for estimating the expiration date of the product and determining whether it is reusable. This makes it possible to accurately evaluate the condition of food and automatically select and suggest the optimal disposal method.

[0691] A "user terminal" refers to a mobile device or computer device carried by a user, and is a device used for sending and receiving data.

[0692] "Means of receiving" refers to the methods and processes for acquiring data from external sources via a network.

[0693] "Means of analysis" refers to the technologies and algorithms used to process received data and understand or judge its content.

[0694] "Evaluating the condition of a product" refers to the act of determining the quality, degree of wear and tear, and usability of a product.

[0695] "Predicted price" refers to an estimated future selling value based on current information.

[0696] "Distribution channels" refer to the routes and media involved in the flow and process of how products and services reach consumers.

[0697] A "supplying organization" refers to an organization or institution that accepts donations of goods or services and operates with the aim of contributing to society.

[0698] "Means for estimating expiration dates and determining whether something is reusable" refers to methods for predicting the usable period of food or products and using that to determine whether recycling or resale is appropriate.

[0699] The system implementing this invention mainly consists of user terminals, a server, and a network connecting them. Mobile devices such as smartphones and tablets are used as user terminals. These terminals have the function of taking pictures of products such as food and transmitting the obtained images to the server in digital format.

[0700] The server uses machine learning models such as TensorFlow to analyze the received image data. Through image analysis, it is possible to automatically evaluate the expiration date and condition of products. Based on the analysis information, a server program using Node.js refers to a database stored in MongoDB and calculates a predicted price for the product based on its market value.

[0701] The server also retrieves the latest information from supplying organizations and informs users of the possibility of donating products. By clearly showing users the conditions and procedures for donation, users can quickly choose between reusing or donating.

[0702] As a concrete example, when a user takes yogurt nearing its expiration date out of the refrigerator and takes a picture of it with the app, the server receives and analyzes this photo. Based on the analysis, options such as selling it at a local flea market or donating it to a local food bank are presented. This entire process helps reduce food waste and promotes the efficient use of resources.

[0703] An example of a prompt message is shown as: "Develop an application that takes a picture of the ingredients left in your refrigerator and suggests the best way to reuse or sell them."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user takes a picture of the product using a device. Through this device operation, the captured image is converted into a digital format and prepared as input data for the system. This input data includes image information, including the product's appearance.

[0707] Step 2:

[0708] The device transmits the captured image data to the server via the network. The input here is the image data from the device, and the output is the same image data that reaches the server. The data is passed to the server through the transmission process.

[0709] Step 3:

[0710] The server applies an image recognition algorithm using TensorFlow to analyze the received image data. The input is image data, and the output is an evaluation value of the product's condition and an estimated expiration date. The data analysis determines the condition and degree of deterioration of the product.

[0711] Step 4:

[0712] The server queries a MongoDB database using a Node.js program based on the analysis, referencing historical product distribution data. The inputs are the product's condition rating and expiration date information, and the output is the predicted selling price. The predicted price is calculated based on market data.

[0713] Step 5:

[0714] The server determines whether an item is eligible for donation and retrieves information about the supplying organization. Inputs include the item's condition rating and expiration date, while outputs include donation conditions and detailed information about the supplying organization. Relevant conditions are considered to determine if an item is suitable for donation.

[0715] Step 6:

[0716] Finally, the server sends the calculated estimated selling price and information about the supplier to the terminal. The input is the result of the server's processing, and the output is the information that the user can view on the terminal. The information is displayed on the terminal screen, and the user can decide whether to reuse or donate the product.

[0717] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0718] This invention is a system aimed at helping users effectively evaluate unwanted items and select appropriate disposal methods. This system is implemented through a user terminal, a server, an emotion engine, and a program connected via a network.

[0719] Users use their mobile devices to take pictures of items they are considering disposing of. Simultaneously, data such as the user's voice and facial expressions are collected during the photo-taking process. This creates a system that obtains information about the user's emotions.

[0720] The device sends captured image data and user sentiment data to the server. The server uses an image recognition algorithm to evaluate the condition of the item and predicts the selling price by referring to a past sales database based on the information obtained.

[0721] Furthermore, the server uses an emotion engine to analyze the user's emotions from the received voice and facial expression data. The analyzed emotional information is used to customize the suggested disposal methods to better reflect the user's feelings. For example, if the user feels an attachment to an item, the emotion engine can highlight the donation option.

[0722] The server sends the terminal an estimated selling price, sentiment-based disposal suggestions, and information on charitable organizations. This information is necessary for the user to decide on the appropriate disposal method for the item.

[0723] Ultimately, users can review the information displayed on their device and choose to sell, donate, or select other options. This system is expected to promote the reuse of items and increase social contribution by allowing users to make decisions that take their emotions into account when disposing of items.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The user takes a picture of unwanted items using the camera on their device. Simultaneously, audio and facial expression data are collected through the device's microphone and camera. This also records the user's emotional response to the items.

[0727] Step 2:

[0728] The terminal processes the captured image data along with the user's voice and facial expression data, and sends it to the server. This data transmission takes place over the network, ensuring secure and efficient communication.

[0729] Step 3:

[0730] The server executes an image recognition algorithm to analyze the received image data. This process includes automatically determining the brand, model, and estimated condition of the item based on its usage.

[0731] Step 4:

[0732] The server calculates a predicted selling price based on the evaluation results of the items and by referring to a historical sales database. This process simultaneously simulates prices across multiple sales channels.

[0733] Step 5:

[0734] The server uses the user's voice and facial expression data to analyze it with an emotion engine and identify the user's emotions towards the item. Based on this data analysis, it formulates an emotion-specific disposal policy.

[0735] Step 6:

[0736] The server compiles information on the predicted selling price, sentiment-based recommended disposal methods, and relevant donation organizations, and sends it to the user's terminal.

[0737] Step 7:

[0738] The terminal displays information received from the server to the user. This display includes advice and suggestions for disposal methods tailored to the user's emotions.

[0739] Step 8:

[0740] Users can choose the appropriate disposal method for items based on the information displayed on their device. This allows for more emotionally responsive decision-making.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] Conventional item valuation systems could perform simple evaluations and calculate transaction prices based on image data of items, but they did not offer disposal methods that took user emotions into consideration. As a result, they only presented uniform disposal options without considering user feelings, which was a problem because it failed to alleviate the psychological burden on users.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes means for receiving digital data captured by an information processing device, means for analyzing the received digital data to evaluate the attributes of an item, and means for analyzing the user's emotional data using an emotion analysis device and customizing the disposal method based on the analysis results. This makes it possible to propose a disposal method that is sensitive to the user's emotions, in addition to an objective evaluation of the item.

[0746] An "information processing device" refers to an electronic device that has the function of acquiring, processing, and transmitting digital data.

[0747] "Digital data" refers to data that is represented in digital format, such as images, sounds, or other information, and is processed electronically.

[0748] "Attributes of an item" refer to the characteristics of the item itself, such as its type, appearance, condition, and function.

[0749] "Predicted transaction price" refers to the future market value of an item calculated based on past transaction data.

[0750] "Transaction intermediary" refers to a channel or platform for buying, selling, or trading goods.

[0751] An "emotion analysis device" refers to a system equipped with the ability to identify and analyze emotions from human voice, facial expressions, and behavior.

[0752] A "donation organization" refers to an organization that accepts donations for social purposes and uses them to carry out public service activities.

[0753] This invention is a system that allows users to evaluate and propose methods for disposing of unwanted items, and is implemented via an information processing device, a server, an emotion analysis device, and a network connecting these components.

[0754] The user takes an image of the item they intend to dispose of using their own information processing device. At the time of taking the image, the user can also provide audio and emotional information related to the item. This information forms the basis for the user's emotion analysis.

[0755] The device transmits image data and emotion data acquired from the user to the server. This process involves data transmission over a network such as the internet.

[0756] The server uses a combination of image recognition algorithms based on TensorFlow and natural language processing techniques suitable for sentiment analysis. This process evaluates the condition of items and calculates a predicted transaction price. Meanwhile, a sentiment analysis device grasps the user's emotions, and based on this, appropriate disposal methods such as selling, donating, or recycling are suggested to the user.

[0757] For example, in a case where a user is disposing of a camera they have used for many years, they can take a picture of the camera and input a voice message such as, "This camera holds many memories." Based on this prompt, the system will assess the camera's market value and present suggestions for donation destinations that resonate with the user's feelings.

[0758] This system combines objective evaluation of items with user sentiment to support appropriate and user-friendly disposal of goods.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The user uses an information processing device to take images of items they are considering disposing of and inputs voice data. The input data consists of image data of the items and voice data indicating the user's emotions. This data is used for subsequent analysis.

[0762] Step 2:

[0763] The terminal transmits image and audio data obtained from the user to the server. The input consists of captured images and recorded audio, which are accurately transmitted to the server as output. This operation is performed using a secure communication protocol.

[0764] Step 3:

[0765] The server analyzes received image data using image recognition algorithms such as TensorFlow. The input is an image of an object; by analyzing the image, it identifies the object's attributes and generates output data about the object's condition and characteristics. Specifically, it determines the type of object and the extent of damage.

[0766] Step 4:

[0767] The server uses this attribute data to calculate a predicted transaction price by referencing past transaction data. The input is attribute data of the goods, and by matching it with the database, it predicts the market price and outputs an estimated market value. This calculation provides a concrete indicator of the selling price.

[0768] Step 5:

[0769] The server analyzes the received audio data using an emotion analysis device and evaluates the user's emotions. The input is the user's audio data, and natural language processing technology is used to analyze the emotional state and generate emotion information as output. This information is used to determine the user's current feelings.

[0770] Step 6:

[0771] The server presents the user with options to sell, donate, or recycle the item, based on its market value and the user's sentiment information. The input is the market value and sentiment information obtained in the previous step, and the program outputs customized options. This operation ensures that suggestions are tailored to the user's emotions.

[0772] Step 7:

[0773] The terminal displays information received from the server on the user's screen. The input is the proposed disposal options, which are visually presented as output on the user interface. The user makes a final decision based on the options presented here.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] When considering the disposal of goods, simply presenting a price without considering the user's feelings makes it difficult to make an appropriate disposal choice that takes into account their attachment to the goods and their emotional value. Conventional systems do not adequately support users in making the most satisfying choice based on their emotions. Therefore, the present invention aims to support disposal choices that take into account the emotional value of goods by providing evaluations of goods and presentations of disposal methods that take into account the user's emotional information.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for receiving image data captured by the user terminal and user emotion data; means for analyzing the received image data to evaluate the condition of the item and analyzing the user emotion data to obtain emotion information; and means for calculating the predicted selling price of the item based on the evaluation results and emotion information, and for suggesting a disposal method that is sensitive to the user's feelings. This makes it possible for the user to choose an appropriate disposal method for the item while also taking their own emotions into consideration.

[0779] A "user terminal" refers to a device, such as a mobile device or computer, used to acquire image data and emotional data of an item.

[0780] "Image data" refers to digital information that represents the appearance and characteristics of an object, expressed as an image taken by a user.

[0781] "Emotional data" refers to digital information that indicates a user's emotional state, obtained from their facial expressions, voice, and other sources.

[0782] A "server" is a central processing unit that receives data from user terminals via a network, performs evaluations of items and sentiment analysis, and returns the results.

[0783] "Evaluating the condition of an item" refers to the act of determining the quality and appearance of an item as captured using image recognition technology.

[0784] "Obtaining emotional information" is the process of analyzing a user's emotional data to determine their emotional state.

[0785] "Calculating a predicted selling price" is the act of calculating an expected selling price based on the results of an assessment of the condition of an item, and by referring to market data.

[0786] "Presenting disposal methods" means showing users options such as selling or donating items, and suggesting recommended methods that take emotional information into consideration.

[0787] In this embodiment of the invention, a user terminal, a cloud-based server, and software necessary for data processing work closely together to provide an item disposal support system that takes the user's emotions into consideration.

[0788] Users take pictures of unwanted items using their own devices, such as smartphones. The devices also have a mechanism to simultaneously capture emotional data, such as the user's voice and facial expressions. The user devices have applications installed for image capture, voice and facial expression analysis, and these data are collected in real time and sent to a cloud server.

[0789] The server analyzes the received image data using OpenCV and TensorFlow, and performs calculations using a machine learning model to evaluate the condition of the items. Next, it calculates a predicted selling price for the items by referring to historical market data. Furthermore, it processes emotional data using Google Cloud's sentiment analysis function to analyze the user's emotional state. Based on these analysis results, it determines whether the user has an attachment to the items and proposes customized and appropriate disposal methods, such as highlighting donation destination information or presenting sales options.

[0790] The results are sent back to the user's terminal from the server, allowing the user to review the information displayed on the screen and select the most suitable disposal method for the items. This process enables users to dispose of items while taking their own feelings and values ​​into consideration, leading to sustainable social contribution.

[0791] As a concrete example, if a user wants to dispose of an old camera, they take a picture of it with the app and send emotional data to the server. The server, recognizing that the user has a strong attachment to the camera, displays an estimated selling price and suggests possible donation options. This allows the user to choose the most suitable disposal method based on their emotions. An example of a prompt to input into the generating AI model would be, "What emotions do you feel about selling this item (e.g., an old camera)? What would be the ideal way to dispose of the item?"

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The user takes pictures of the items they are considering disposing of using their user device. At this time, the device's camera and microphone are used to acquire image data of the items and the user's emotional data (voice and facial expression data). The input consists of images of the items, voice, and facial expression data, which are then prepared for transmission to the server.

[0795] Step 2:

[0796] The device sends the acquired image data and emotion data to a cloud server. It uses image data of objects and emotion data as input, and is configured to package and securely transmit the data. The cloud server holds the data in the format required for subsequent processing.

[0797] Step 3:

[0798] The server analyzes the received image data using image processing software (OpenCV). The input is image data of an item, which is used to recognize the item's features, and its condition is evaluated using an evaluation model (a constructed machine learning model). Based on the condition of the item output by this process, data is obtained to measure its value.

[0799] Step 4:

[0800] The server retrieves the item's condition assessment results and historical market data from a database, and uses this as input to calculate a predicted selling price. Data analysis using TensorFlow outputs the potential selling price of the item in the market.

[0801] Step 5:

[0802] The server processes voice and facial expression data using an emotion analysis engine. Using Google Cloud's emotion analysis API, it analyzes the emotion data as input and outputs user emotion information. This output is used to customize the disposal method, which will be proposed later.

[0803] Step 6:

[0804] The server integrates predicted selling price and sentiment information to suggest the disposal method (sale, donation, etc.) that best suits the user's psychological state. It uses predicted selling price and sentiment information as input, generates prompt sentences using a generative AI model, and outputs the optimal disposal suggestion.

[0805] Step 7:

[0806] The server sends the proposed details (predicted selling price, suggested disposal method) to the user's terminal. The output information includes highlighted recipient information and suggested sales options, which the user's terminal displays on its screen.

[0807] Step 8:

[0808] The user reviews the information provided through the terminal and makes a decision about disposing of the items based on their own feelings. After the user's selection, the entire system process is completed.

[0809] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0810] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0811] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0812] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0813] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0814] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0815] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0816] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0817] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0818] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0819] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0820] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0821] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0822] 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.

[0823] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0824] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0825] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0826] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0827] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0828] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0829] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means for receiving image data captured by a user terminal,

[0833] A means for analyzing received image data to evaluate the condition of an item,

[0834] A method for calculating the predicted selling price of goods based on the evaluation results,

[0835] A means of presenting the calculated selling price based on multiple sales channels,

[0836] A means of obtaining and displaying information on donation fundraising organizations,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which uses a past sales database in calculating the predicted selling price.

[0840] (Claim 3)

[0841] The system according to claim 1, which applies a deep learning model to the evaluation of the condition of an item.

[0842] "Example 1"

[0843] (Claim 1)

[0844] A means for receiving visual media acquired by a user information device,

[0845] A means for automatically evaluating the state of an object by analyzing a received visual medium,

[0846] A means for calculating the predicted market price of an item based on the evaluation results,

[0847] A means of presenting calculated market prices based on multiple commercial transaction methods,

[0848] Means for obtaining and displaying information about charitable organizations,

[0849] A means for determining the disposal method of an item based on a selection entered from an information device,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which uses historical transaction records in calculating predicted market prices.

[0853] (Claim 3)

[0854] The system according to claim 1, which applies a machine learning model to the evaluation of the condition of an item.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] A means of receiving data captured by the user's terminal,

[0858] A means of analyzing received data to evaluate the condition of the product,

[0859] A means for calculating the predicted price of a product based on the evaluation results,

[0860] A means of presenting the calculated price based on multiple distribution channels,

[0861] A means of obtaining and displaying information about supplying organizations,

[0862] A means of estimating the expiration date of a product and determining whether it is reusable,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, which uses a historical transaction database in calculating the predicted price.

[0866] (Claim 3)

[0867] The system according to claim 1, which applies a machine learning model to the evaluation of the condition of a product.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A means for receiving digital data captured by an information processing device,

[0871] A means for analyzing received digital data and evaluating the attributes of an item,

[0872] A method for calculating the predicted transaction price of goods based on the evaluation results,

[0873] A means of presenting the calculated transaction price based on multiple transaction media,

[0874] A means of analyzing user emotional data using an emotion analysis device and customizing disposal methods based on the analysis results,

[0875] A means of obtaining and displaying information about donation organizations,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which uses time-series trading data in calculating predicted trading prices.

[0879] (Claim 3)

[0880] The system according to claim 1, which applies a machine learning model to the attribute evaluation of an item.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] A means for receiving image data captured by the user's terminal and user emotion data,

[0884] A means of analyzing received image data to evaluate the condition of an item and analyzing user emotion data to obtain emotional information,

[0885] A means of calculating the predicted selling price of an item based on evaluation results and emotional information, and presenting disposal methods that are sensitive to the user's psychology,

[0886] A means of presenting the calculated sales price based on multiple sales methods, and highlighting donation recipient information based on emotional information,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which uses a historical database in calculating the predicted selling price.

[0890] (Claim 3)

[0891] The system according to claim 1, which applies a machine learning model to the evaluation of the condition of an item. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving image data captured by a user terminal, A means for analyzing received image data to evaluate the condition of an item, A method for calculating the predicted selling price of goods based on the evaluation results, A means of presenting the calculated selling price based on multiple sales channels, A means of obtaining and displaying information on donation fundraising organizations, A system that includes this.

2. The system according to claim 1, which uses a past sales database in calculating the predicted selling price.

3. The system according to claim 1, which applies a deep learning model to the evaluation of the condition of an item.

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