system

Abstract

We provide the system. [Solution] A means of taking and registering images of personal belongings, A means of analyzing captured images using a machine learning model and extracting characteristic information of objects, A means for predicting the current market value of an item based on analyzed characteristic information by referring to market transaction data, By analyzing past market trends and seasonal demand, we can recommend the best time to sell goods. A means of selecting a reliable buyer and presenting it to the user, A means of providing users with reviews and evaluation information on the buyer, 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 method for controlling a persona chatbot, which is performed by at least one processor, 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] It is not easy to quantitatively grasp the market value of an item owned by an individual and determine the optimal trading timing. In particular, high-end or rare items owned by an individual are difficult to trade effectively without rich trading experience and market knowledge. As a result, problems arise where potential profits cannot be maximized due to overlooking asset value or choosing an inappropriate trading time. This invention was devised to appropriately evaluate the market value of these individual items and support optimal trading decisions.

Means for Solving the Problems

[0005] The present invention provides a system that allows individuals to easily grasp the market value of their assets and realize asset transactions under optimal conditions, by including means for taking and registering images of items owned by an individual, means for analyzing images using a machine learning model and extracting feature information, means for predicting the current market value of items by referring to market transaction data, means for recommending the optimal selling time by analyzing past market trends and seasonal demand, means for selecting and presenting reliable buyers, and means for providing reviews and evaluation information of buyers.

[0006] "Personally owned items" refers to physical objects owned by individual users for personal use.

[0007] "Method of taking and registering images" refers to the process by which a user takes pictures of their belongings and inputs the information into the system.

[0008] A "machine learning model" refers to a set of algorithms that use large amounts of data to enable computers to learn specific patterns and features and automatically perform analysis and predictions.

[0009] "Extracting feature information" refers to the process of analyzing image data obtained from an item to identify its characteristics and related information.

[0010] "Market transaction data" refers to data that includes records of buying and selling goods in the past and present market, as well as price information.

[0011] "Means of predicting market value" refers to the process of estimating the current and future market prices of an item based on collected data and extracted characteristics.

[0012] "Past market trends" refers to analyzing market trends and price fluctuation patterns of goods based on historical data.

[0013] "Seasonal demand" refers to fluctuations in consumer demand in the market that occur during specific seasons or periods.

[0014] "Methods for recommending the timing of a sale" refers to a process that, based on collected and analyzed data, indicates the optimal time to sell an item to maximize profits.

[0015] A "reliable buyer" refers to a buyer or market that can provide stable transactions in terms of track record and reputation.

[0016] "Review and rating information" refers to information provided by other users and business partners, including opinions and ratings about trading partners such as buyers. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0020] 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.

[0021] 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.

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

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] The present invention provides information to understand the market value of personal belongings and to optimize their buying and selling. This system primarily consists of a process of collecting and analyzing information about items and providing recommendation information through communication between a terminal and a server.

[0039] Users take photos of their belongings with their smartphone or other camera and register the images on their device. It is recommended that users enter basic information about the item (e.g., product name, model number, and year of manufacture) during registration. This allows users to directly incorporate item data into the system in digital format from the initial stages.

[0040] The terminal transfers the registered images and information to the server. The server first analyzes the received image data using a machine learning model to identify the characteristics of the item. For example, if it is a luxury watch, it extracts the brand, model name, serial number, etc. Based on this information, the server matches it with market transaction data to predict the current market value of the item.

[0041] Furthermore, the server recommends the optimal selling time to the user based on past market trends and seasonal demand. This involves analyzing historical transaction data to identify price fluctuation cycles and demand timing.

[0042] In addition, the server selects and presents information on reliable buyers, such as highly-rated buyers. This information serves as a guide for users to conduct transactions under the most favorable conditions. Users can compare the conditions and ratings of the presented candidates and make the most appropriate choice.

[0043] For example, when a user wants to sell a luxury car, they take pictures of the vehicle with their smartphone and register them in the application. The server analyzes the images, refers to the vehicle model and current market conditions, and calculates a predicted market price. Furthermore, based on past price trends of similar vehicles, it can recommend that the optimal time to sell would be a few months from now. Based on this information, the user can select the company offering the best terms from the listed buyers and proceed with negotiations.

[0044] As described above, the system of the present invention helps users accurately grasp the market value of their owned items and conduct buying and selling activities efficiently and effectively.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users use their smartphones to take pictures of the items they are considering selling and enter item information through the app. This information includes product name, model, and year of manufacture.

[0048] Step 2:

[0049] The device transfers image data and related information provided by the user to the server. The data is encrypted and transmitted securely.

[0050] Step 3:

[0051] The server analyzes the received images using a machine learning model to extract characteristic information about the items. For example, it identifies the brand and model of a watch, or the manufacturer and model number of a car.

[0052] Step 4:

[0053] The server references a market transaction database and predicts the current market value of an item based on extracted characteristic information. This utilizes price data from similar past transactions.

[0054] Step 5:

[0055] The server analyzes past market trends and seasonal demand to calculate the optimal time to sell. The analysis is based on past price fluctuation patterns.

[0056] Step 6:

[0057] The server selects and presents reliable buyers to the user based on their geographical location and the type of items they sell. Evaluation information for the buyers is also provided.

[0058] Step 7:

[0059] Based on the information provided by the user, the buyer and timing of the sale are determined. Details of the selected company and sale timing can be viewed through the app.

[0060] (Example 1)

[0061] 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."

[0062] This solution addresses the challenge of quickly and accurately determining the fair market value of personal belongings and finding the optimal time and reliable buyer for sale.

[0063] 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.

[0064] In this invention, the server includes means for analyzing images acquired using an artificial intelligence model and extracting characteristic information of an item; means for referring to market transaction information and predicting the current market value of the item based on the analyzed characteristic information; and means for analyzing past market trends and timing characteristics and recommending the optimal time to sell the item. This makes it possible for individuals to accurately grasp the market value of items they own and to conduct efficient buying and selling transactions.

[0065] An "individual" is a sole natural person who owns a particular item and intends to buy or sell it.

[0066] "Goods" refer to specific products or possessions owned by an individual that are subject to buying and selling.

[0067] "Video" refers to digital images or video files that visually record objects.

[0068] "Registration" refers to the process of inputting acquired video footage and related information into the system and saving it to the database.

[0069] An "artificial intelligence model" is a computational method that incorporates machine learning algorithms for data analysis and prediction.

[0070] "Characteristic information" refers to specific attributes and data points that are useful for identifying and classifying items.

[0071] "Market transaction information" refers to data on past and present market buying and selling prices and demand trends.

[0072] "Market value" refers to the expected buying and selling price or valuation of an item in the current market environment.

[0073] "Market trends" refer to the tendencies of supply and demand fluctuations and price changes in the market over time.

[0074] "Periodic characteristics" refer to the characteristics of market demand and price fluctuations during a specific period.

[0075] A "business partner" refers to a reliable company or entity in the buying and selling of goods.

[0076] "Evaluation information" refers to information based on opinions and ratings from other users regarding a business partner.

[0077] The system in this invention aims to quickly and accurately determine the market value of personal belongings and to find the optimal time for sale and a reliable buyer. This system operates using terminals and servers.

[0078] The user first takes a photograph of an item they own using a mobile device such as a smartphone. The captured image is registered using a dedicated application on the device. This application provides an interface that allows the user to input basic information about the item (e.g., item name, model number, year of manufacture) along with the image.

[0079] The terminal transmits registered video and information to the server. This transmission uses a secure communication method via the internet. The server analyzes the received video data using generative AI models such as Convolutional Neural Networks (CNNs) to extract characteristic information about the objects. This allows the server to recognize what the objects are and how they should be classified.

[0080] Next, the server references the characteristic information and compares it with a large-scale market transaction information database it maintains internally. This database stores information on past transaction prices and market supply and demand trends. Based on this data, the server predicts the market value of the item and provides it to the user.

[0081] Furthermore, the server has the capability to analyze historical market trend data and seasonal characteristics. This allows it to identify the optimal selling time and recommend it to the user. It also uses a reliability-based selection algorithm to present highly-rated trading partner information. This information includes evaluations and reviews from past users.

[0082] For example, if a user wants to sell a rare book, they capture an image of the book on their device and send the data to the server. The server analyzes the image to extract the book's title and edition information and provides a corresponding market value. Based on past transaction data, if it determines that summer is particularly suitable for selling, it will recommend this to the user. A list of reliable trading partners is also provided, allowing the user to choose the method that best suits their needs from the available options.

[0083] An example of a prompt message might be, "Please tell me the current market value and recommended selling time for the painting (artist name, title, year of creation) that you intend to sell."

[0084] Thus, the system of the present invention helps users efficiently and effectively understand the value of goods and conduct optimal buying and selling activities.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user uses their smartphone camera to take a picture of an item they own. The input is the captured digital image. The user selects this image in a dedicated application on their device and registers it along with basic information about the item (e.g., item name, model number, year of manufacture). The output is a dataset containing the item's image and associated basic information, which is saved on the device.

[0088] Step 2:

[0089] The terminal transmits image data and basic information of registered items to the server. The input is the previously saved dataset, and the output is the digital packet it is transferred to the server. The terminal uses a secure communication protocol over the internet to maintain data integrity and confidentiality.

[0090] Step 3:

[0091] The server processes the received data and first inputs the image data into an AI model. Specifically, it performs image analysis using a Convolutional Neural Network (CNN) to extract feature information of the items. The input is image data of the items, and the output is feature information of the items. This analysis identifies the conditions under which an item is sold and the identification data (e.g., brand or model).

[0092] Step 4:

[0093] The server uses the extracted feature information to query its internal market transaction information database. The input is feature information, and by performing a database search, the predicted market value of the item is obtained as output. The database records past and present market trends, and the value of the item is calculated based on this.

[0094] Step 5:

[0095] The server analyzes historical market trend data and seasonal characteristics to calculate the optimal selling time. The input is market transaction information and seasonal characteristics, and the output is the optimal selling time presented to the user. This allows the user to create a selling plan that takes price fluctuations into account.

[0096] Step 6:

[0097] The server collects reliable trading partner information and selects suitable buyers. Input is past evaluation and review information, and output is recommended trading partner information. Users can use this information to make informed decisions about which buyer will offer the most favorable terms.

[0098] (Application Example 1)

[0099] 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."

[0100] When individuals sell their personal belongings, accurately determining their market value, finding the optimal selling time, and discovering a trustworthy buyer are all challenging. Furthermore, understanding market trends and demand in real time and conducting transactions under the most favorable conditions is also a challenge. Moreover, there is a lack of readily available means for users to access this information.

[0101] 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.

[0102] In this invention, the server includes means for capturing digital images of items owned by an individual and registering them in an information processing device; means for analyzing the captured digital images using a machine learning algorithm and automatically extracting characteristic information of the items; and means for referring to transaction information in a database and predicting the current market value of the items based on the analyzed characteristic information. This allows individuals to understand the market value of their owned items in real time, easily find the optimal time to sell and a reliable buyer, and conduct transactions under the most favorable conditions.

[0103] "Means of taking digital images of personal belongings and registering them in an information processing device" refers to a method of taking images of personal belongings with a digital camera or similar device, and inputting and saving that image data into a system.

[0104] "A means of analyzing digitally captured images using machine learning algorithms and automatically extracting characteristic information of items" refers to a method that applies machine learning technology to analyze registered images and automatically identify the brand, model number, and other identifiers of an item.

[0105] "A means of predicting the current market value of an item based on analyzed characteristic information by referring to transaction information in a database" refers to a process for predicting the price of an identified item by utilizing past and present market transaction data in a collected database.

[0106] "A means of analyzing temporal market trends and cyclical demand to calculate and provide the optimal time to sell goods" refers to a method of analyzing market data to identify price fluctuations and demand peaks, and then determining the optimal timing for selling based on those insights.

[0107] "Means of selecting and presenting reliable selling institutions to users" refers to the process of identifying trustworthy vendors as suppliers of goods, taking into account evaluation data and reliability indicators, and providing users with a list of such vendors.

[0108] "Means of providing users with evaluation indicators and evaluation information for selling institutions" refers to a system that presents reviews and evaluation scores of companies that users can use as a reference when selecting a buyer.

[0109] "A means of providing an application program that can be executed on a user's mobile device, thereby presenting market value information and buyer information" refers to a method of providing an application that operates on a mobile device such as a smartphone or tablet and displays price information and buyer information.

[0110] The system for realizing this invention primarily uses mobile information terminals such as smartphones and tablets, and a cloud server. Users take digital images of their belongings with their smartphones and send those images to the server through a dedicated application. The terminals are equipped with an interface for taking photos and inputting them into the application.

[0111] On the server, the backend runs using Flask, built with Python, and TENSORFLOW® machine learning algorithms analyze the received image data. This analysis automatically extracts the brand, model, and other characteristic information of the items. Based on this characteristic information, the server runs a program that references market transaction data stored in a database to predict the current market value.

[0112] Furthermore, the server analyzes past market trends and seasonal demand data to calculate the most profitable time to sell goods. It also selects reliable selling agencies and provides users with a list of these agencies based on evaluation criteria. Based on this information, users can determine the optimal timing and conditions for their transactions and then execute them.

[0113] As a concrete example, suppose an individual wants to sell an old wristwatch. They take a picture of it and send it to the system. The system identifies the watch's model and brand and displays its market value. At the same time, it recommends that the best time to sell is before the next new model is released. In this way, users can easily sell their watches under better conditions.

[0114] An example of a prompt using a generative AI model is as follows: "Analyze the market value of this item and suggest the optimal time to sell it and a reliable buyer."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] Users use their mobile devices to take digital images of items they wish to sell and send the images, along with necessary basic information, to the server via a dedicated application. Input includes the product name, model number, and the captured image file. Output is a notification that the transmission to the server is complete.

[0118] Step 2:

[0119] The server begins processing the received image data and item information, analyzing the images using a machine learning algorithm powered by TensorFlow. This automatically extracts the brand name and model number from the input image. The output is item feature information.

[0120] Step 3:

[0121] The server calculates market value by referencing past transaction information in the database based on the generated feature information. The inputs used are feature information and transaction data, and by matching these, the server predicts the current market price. The output is the predicted market value.

[0122] Step 4:

[0123] The server uses market value results and analyzes past market trends and seasonal demand to calculate the optimal selling time. The input for data analysis includes market value and trend data, and the output is the recommended selling time.

[0124] Step 5:

[0125] The server selects reliable selling agencies based on evaluation metrics and creates a list of candidates. This process uses agency evaluation data as input and outputs a list of reliable agencies.

[0126] Step 6:

[0127] The server returns the final analysis results to the user's terminal, including market value, optimal selling time, and a list of reliable buyers. The input includes the output results from the previous stage, and the output is the display of information in the user's application.

[0128] Step 7:

[0129] The user makes a sale decision based on the information provided. The application then displays an interface where the user can select the next action. The input includes the user's selection, and the output is the selection of the next action.

[0130] 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.

[0131] This invention combines a system that appropriately evaluates the market value of personal belongings and recommends the optimal timing and trading partners for their sale with an emotion engine that recognizes and utilizes the user's emotions. This system mainly consists of a user terminal, a server, and an emotion recognition module.

[0132] Users take pictures of items they are considering selling using a smartphone or other device and input the information within the app. During the input process, the app captures the user's facial expressions and voice to recognize emotions. At this time, an emotion engine analyzes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, etc.) in real time. This information is reflected in the operation of the entire system and used to customize recommended information.

[0133] The terminal sends image data and information registered by the user to the server. The server applies a machine learning model and identifies the characteristics of the item through image analysis. Based on the extracted characteristic information, a process is carried out to predict the market value of the item by referring to market transaction data. Furthermore, the server analyzes past market trends to recommend the optimal time to sell. It also takes into account sentiment data recognized by the sentiment engine and presents the user with optimized buyer information and transaction conditions.

[0134] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the user takes a photo of the watch through the app, and the app detects a slight smile on their face during the photo shoot. This emotional state suggests the user has a positive view of the watch's market value and their expectations for selling it. The server incorporates this emotional information and recommends leading buyers while simultaneously emphasizing positive language to encourage immediate transactions.

[0135] This allows for a more personalized trading experience that takes into account the user's emotions, intention to sell, and level of trust. The system aims to improve the efficiency and comfort of the asset buying and selling process by utilizing user emotions.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] When a user takes pictures of items they are considering selling with their smartphone, they register the images through the application and input basic information about the items. During this process, the user's facial expressions and voice data are collected by the device.

[0139] Step 2:

[0140] The device uses an emotion engine to analyze the user's emotional state in real time from their facial expressions and voice data. The analysis results (for example, whether the user is satisfied or anxious) are digitized and sent to the server along with other data.

[0141] Step 3:

[0142] The server analyzes the received image data using a machine learning model to extract the characteristics of the items (brand name, model, condition, etc.). This characteristic information, combined with a market transaction database, serves as a basis for predicting the market value of the items.

[0143] Step 4:

[0144] The server analyzes past market trends and seasonal demand data to recommend the best time to sell items. It then considers user sentiment data and adjusts how the optimal timing is presented (e.g., using more positive content).

[0145] Step 5:

[0146] The server selects and presents reliable buyers to the user based on market data and sentiment information. This includes evaluation information of the buyers, and recommendation language is used that is tailored to the user's emotional state.

[0147] Step 6:

[0148] Based on the information provided by the user, decisions regarding the buyer and timing of the sale are made. The user's selection is registered in the system, and the transaction begins. During this process, if the user's feelings change, this is recognized again and reflected in the data.

[0149] Step 7:

[0150] After a transaction is completed, the server analyzes the user's sentiment data along with the entire transaction history, and uses this data to improve the system and enhance personalization in the future. This data forms the basis for improving the quality of service in subsequent transactions.

[0151] (Example 2)

[0152] 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".

[0153] Traditional asset trading systems made recommendations based solely on market data, without considering user emotions or subjective judgments. This resulted in insufficient consideration of user satisfaction when making trading decisions. Consequently, there was a risk of decreased user confidence in the recommendations provided and reduced satisfaction with trading results.

[0154] 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.

[0155] In this invention, the server includes means for capturing and registering images of items owned by an individual, means for analyzing the user's facial expressions and voice using emotion recognition technology to acquire emotion data, and means for customizing sales information taking the emotion data into consideration. This makes it possible to provide personalized sales recommendations based on the user's emotions.

[0156] "Images of items" refer to digital information that visually captures the items being considered for sale.

[0157] A "machine learning model" is a technology that includes algorithms for analyzing data such as images and identifying features and patterns based on that data.

[0158] "Market value" refers to the expected price or economic value of a particular item when it is traded in the market.

[0159] "Market trends" refer to market movements and general tendencies based on past trading data and supply and demand fluctuations.

[0160] "Emotion recognition technology" is a technology that identifies a person's emotional state from their facial expressions and voice.

[0161] "Emotional data" refers to information that expresses a user's emotions in a numerical or categorical form.

[0162] A "buyer" refers to a trading partner, such as a business or individual, who may potentially purchase the goods.

[0163] "Customization" is the process of optimizing and individualizing information and services based on specific criteria and conditions.

[0164] This invention is a system that appropriately evaluates the market value of personal belongings and recommends the optimal buying and selling timing and trading partners, incorporating technology to recognize and utilize user emotions. The system mainly consists of a user terminal, a server, and an emotion recognition module.

[0165] Users take pictures of items they are considering selling using a smartphone or other device, and input information about the items within the app. The device used here is a personal digital assistant (PDA) equipped with a camera and microphone for image processing and emotion recognition.

[0166] The terminal sends the input image data and item information to the server. The server analyzes the images using a machine learning model and extracts feature information about the items. This machine learning model includes a general-purpose neural network for image recognition. This feature information is used to predict the current market value of the items by referencing a market transaction database.

[0167] Furthermore, the terminal captures the user's facial expressions and voice through an emotion recognition module to obtain the user's emotional data. This emotional data reflects the user's expectations and level of trust regarding the transaction. This emotional data is considered by the server because it influences the selection of proposed buyers and the presentation of sale terms.

[0168] The server further analyzes past market trends and seasonal demand based on the acquired data, providing recommendations for the optimal time to sell items. In this way, users receive personalized selling suggestions based on emotion, allowing them to sell under the most favorable conditions.

[0169] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the emotion engine detects that the user is slightly relieved as they take photos of the watch. Based on this emotion data, the server prioritizes recommending favorable and trustworthy buyers and provides the user with information to encourage immediate transaction.

[0170] An example of a prompt to input into a generating AI model is: "Please describe a system that evaluates the market value of an item a user is considering selling, based on images and sentiment data related to the item, and recommends the best buyer and timing."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user takes a picture of the item they are considering selling using their device's camera and enters detailed information about the item (brand, model, year of purchase, etc.) into the app. This data is temporarily stored on the device as the item's basic information. This information is then sent to the server in the next step.

[0174] Step 2:

[0175] The device captures the user's facial expressions and voice, and uses a built-in emotion recognition module to analyze the user's emotional data in real time. Inputs include camera video and audio data, and output is the analyzed emotional state (e.g., satisfaction, dissatisfaction, interest, etc.). This emotional data is stored as data indicating the user's attitude towards the transaction.

[0176] Step 3:

[0177] The device transmits collected image data, item information, and emotion data to the server. This data transmission is performed using a secure communication protocol, and the server receives and stores the data.

[0178] Step 4:

[0179] The server receives image data and analyzes the features of the objects using a machine learning model. Specifically, it performs image recognition using a neural network to extract feature information about the objects. The input is image data, and the output is a feature vector that identifies the objects.

[0180] Step 5:

[0181] The server uses the item's characteristic information to refer to a market transaction database and predict the item's market value. Using database queries and statistical models, the system takes the characteristic information as input and generates a predicted market price as output.

[0182] Step 6:

[0183] The server analyzes past market trends and proposes the optimal selling time. This analysis uses time-series data analysis and takes market fluctuation patterns into consideration. As a result, the server proposes a recommended selling time to the user.

[0184] Step 7:

[0185] Based on recognized sentiment data, the server presents the user with buyers and transaction terms optimized for them. This process takes sentiment data as input and outputs buyers and terms likely to be favorable to the user. If positive sentiment is detected, information encouraging immediate transactions is highlighted.

[0186] (Application Example 2)

[0187] 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".

[0188] When individuals sell their belongings, they need optimal market valuation, the right timing for the sale, and reliable buyers. However, providing personalized selling recommendations that take user emotions into consideration is difficult. Furthermore, standard selling processes often fail to adequately reflect user intentions and feelings, resulting in lower satisfaction.

[0189] 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.

[0190] This invention includes a server that includes means for acquiring and registering visual data of items owned by an individual; means for analyzing the acquired visual data using a machine learning model and extracting characteristic information of the items; means for referencing market transaction information and evaluating the current market value of the items based on the analyzed characteristic information; means for analyzing past market trends and demand forecasts and recommending the optimal time to sell the items; and means for evaluating the user's emotional state in real time using an emotion analysis engine and optimizing the user's intention to sell the items. This makes it possible to provide personalized recommendations based on the user's emotions and improve satisfaction with the selling process.

[0191] "Visual data of personal belongings" refers to image and video data acquired to identify personal belongings owned by an individual.

[0192] A "machine learning model" is a computational algorithm that extracts features from data and learns patterns to make future predictions and classifications.

[0193] "Characteristic information" refers to information that indicates the unique characteristics of an item, and it serves as basic data for evaluating its market value.

[0194] "Market transaction information" refers to data including the history of buying and selling goods in the market and price trends, and is used to evaluate the market value of goods.

[0195] "Past market trends" refers to data that shows the history of price fluctuations and demand in the market in the past.

[0196] "Demand forecasting" refers to the results of an analysis aimed at predicting future trends in market demand for goods.

[0197] "Optimal time" refers to the period when it is believed that the greatest profit can be obtained when selling an item.

[0198] An "emotion analysis engine" is a software system that evaluates a user's emotional state in real time by analyzing their facial expressions and voice.

[0199] "User emotional state" refers to information that indicates the psychological state of the user that influences their decision to sell an item.

[0200] "Personalized recommendations" refer to providing information and suggestions that are customized according to the individual user's characteristics and emotions.

[0201] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server works in conjunction with a terminal that has the function of acquiring visual data of items owned by an individual, and extracts characteristic information of the items using a machine learning model based on that visual data. Specifically, the terminal uses a camera device such as a smartphone to take visual data of items that the user is considering selling, and transmits this data to the server.

[0202] The server uses Microsoft Azure's Face API to perform facial recognition and emotion analysis on the received visual data. It also uses Google Cloud's Speech-to-Text API to convert speech input into text, and uses this information to evaluate the user's emotional state. The extracted characteristic information is stored in Amazon Web Services (AWS) DynamoDB and cross-referenced with market transaction data.

[0203] Market transaction information is analyzed using historical market trends and demand forecast data collected by the server and used to calculate the optimal selling time. Based on the user's emotional state revealed by sentiment analysis, the server suggests personalized buyers and timing. For example, if a user shows a strong interest in a luxury watch, the server will notify the user's terminal in real time of the best buyers and recommendations related to that watch.

[0204] As a specific use case, when a user tries to assess the value of household items using their smartphone, the system performs emotion analysis when the camera takes a picture of an item. If positive emotions are detected, it immediately highlights the item as a recommendation to sell. Prompt messages such as "Analyze the facial expressions the user makes while browsing a product page and notify them of special offers related to that product if they show interest" are used as a reference to enrich the user experience.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The device uses its camera to photograph items the user is considering selling, acquiring visual data. This visual data is sent to the server as input from the user. The output is transferred to the server as image data.

[0208] Step 2:

[0209] The server analyzes the received image data using a machine learning model to extract characteristic information about the items. The input is image data, and the output is characteristic information about the items derived from the images. This process uses a characteristic extraction algorithm.

[0210] Step 3:

[0211] The server evaluates the market value of an item by referencing market transaction information based on its characteristic data. The input is the characteristic data, and the output is the evaluated market value. This process utilizes data matching with historical transaction data and a valuation model.

[0212] Step 4:

[0213] The server analyzes historical market trends and demand forecast data to calculate the optimal selling time. The input is market value information, and the output is a recommendation for the optimal selling time. This analysis uses a trend analysis algorithm.

[0214] Step 5:

[0215] The device uses the user's camera and microphone to acquire facial and audio data, and an emotion analysis engine evaluates the user's emotional state. The input is the user's emotional data, and the output is the analyzed emotional state. Emotion recognition technology is used in this process.

[0216] Step 6:

[0217] The server suggests personalized buyers and selling conditions to the user based on their emotional state and market value information. The input is emotional state and market value information, and the output is individually customized recommendations. This step involves individual optimization by an AI model.

[0218] Step 7:

[0219] The user receives recommendations from the server on their device and selects the appropriate action. This results in a personalized selling experience. The input is the recommendations from the server, and the output is the user's decision.

[0220] 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.

[0221] 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 those described above. 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 shown 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] The present invention provides information to understand the market value of personal belongings and to optimize their buying and selling. This system primarily consists of a process of collecting and analyzing information about items and providing recommendation information through communication between a terminal and a server.

[0237] Users take photos of their belongings with their smartphone or other camera and register the images on their device. It is recommended that users enter basic information about the item (e.g., product name, model number, and year of manufacture) during registration. This allows users to directly incorporate item data into the system in digital format from the initial stages.

[0238] The terminal transfers the registered images and information to the server. The server first analyzes the received image data using a machine learning model to identify the characteristics of the item. For example, if it is a luxury watch, it extracts the brand, model name, serial number, etc. Based on this information, the server matches it with market transaction data to predict the current market value of the item.

[0239] Furthermore, the server recommends the optimal selling time to the user based on past market trends and seasonal demand. This involves analyzing historical transaction data to identify price fluctuation cycles and demand timing.

[0240] In addition, the server selects and presents information on reliable buyers, such as highly-rated buyers. This information serves as a guide for users to conduct transactions under the most favorable conditions. Users can compare the conditions and ratings of the presented candidates and make the most appropriate choice.

[0241] For example, when a user wants to sell a luxury car, they take pictures of the vehicle with their smartphone and register them in the application. The server analyzes the images, refers to the vehicle model and current market conditions, and calculates a predicted market price. Furthermore, based on past price trends of similar vehicles, it can recommend that the optimal time to sell would be a few months from now. Based on this information, the user can select the company offering the best terms from the listed buyers and proceed with negotiations.

[0242] As described above, the system of the present invention helps users accurately grasp the market value of their owned items and conduct buying and selling activities efficiently and effectively.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] Users use their smartphones to take pictures of the items they are considering selling and enter item information through the app. This information includes product name, model, and year of manufacture.

[0246] Step 2:

[0247] The device transfers image data and related information provided by the user to the server. The data is encrypted and transmitted securely.

[0248] Step 3:

[0249] The server analyzes the received images using a machine learning model to extract characteristic information about the items. For example, it identifies the brand and model of a watch, or the manufacturer and model number of a car.

[0250] Step 4:

[0251] The server references a market transaction database and predicts the current market value of an item based on extracted characteristic information. This utilizes price data from similar past transactions.

[0252] Step 5:

[0253] The server analyzes past market trends and seasonal demand to calculate the optimal time to sell. The analysis is based on past price fluctuation patterns.

[0254] Step 6:

[0255] The server selects and presents reliable buyers to the user based on their geographical location and the type of items they sell. Evaluation information for the buyers is also provided.

[0256] Step 7:

[0257] Based on the information provided by the user, the buyer and timing of the sale are determined. Details of the selected company and sale timing can be viewed through the app.

[0258] (Example 1)

[0259] 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."

[0260] This solution addresses the challenge of quickly and accurately determining the fair market value of personal belongings and finding the optimal time and reliable buyer for sale.

[0261] 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.

[0262] In this invention, the server includes means for analyzing images acquired using an artificial intelligence model and extracting characteristic information of an item; means for referring to market transaction information and predicting the current market value of the item based on the analyzed characteristic information; and means for analyzing past market trends and timing characteristics and recommending the optimal time to sell the item. This makes it possible for individuals to accurately grasp the market value of items they own and to conduct efficient buying and selling transactions.

[0263] An "individual" is a sole natural person who owns a particular item and intends to buy or sell it.

[0264] "Goods" refer to specific products or possessions owned by an individual that are subject to buying and selling.

[0265] "Video" refers to digital images or video files that visually record objects.

[0266] "Registration" refers to the process of inputting acquired video footage and related information into the system and saving it to the database.

[0267] An "artificial intelligence model" is a computational method that incorporates machine learning algorithms for data analysis and prediction.

[0268] "Characteristic information" refers to specific attributes and data points that are useful for identifying and classifying items.

[0269] "Market transaction information" refers to data on past and present market buying and selling prices and demand trends.

[0270] "Market value" refers to the expected buying and selling price or valuation of an item in the current market environment.

[0271] "Market trends" refer to the tendencies of supply and demand fluctuations and price changes in the market over time.

[0272] "Periodic characteristics" refer to the characteristics of market demand and price fluctuations during a specific period.

[0273] A "business partner" refers to a reliable company or entity in the buying and selling of goods.

[0274] "Evaluation information" refers to information based on opinions and ratings from other users regarding a business partner.

[0275] The system in this invention aims to quickly and accurately determine the market value of personal belongings and to find the optimal time for sale and a reliable buyer. This system operates using terminals and servers.

[0276] The user first takes a photograph of an item they own using a mobile device such as a smartphone. The captured image is registered using a dedicated application on the device. This application provides an interface that allows the user to input basic information about the item (e.g., item name, model number, year of manufacture) along with the image.

[0277] The terminal transmits registered video and information to the server. This transmission uses a secure communication method via the internet. The server analyzes the received video data using generative AI models such as Convolutional Neural Networks (CNNs) to extract characteristic information about the objects. This allows the server to recognize what the objects are and how they should be classified.

[0278] Next, the server references the characteristic information and compares it with a large-scale market transaction information database it maintains internally. This database stores information on past transaction prices and market supply and demand trends. Based on this data, the server predicts the market value of the item and provides it to the user.

[0279] Furthermore, the server has the capability to analyze historical market trend data and seasonal characteristics. This allows it to identify the optimal selling time and recommend it to the user. It also uses a reliability-based selection algorithm to present highly-rated trading partner information. This information includes evaluations and reviews from past users.

[0280] For example, if a user wants to sell a rare book, they capture an image of the book on their device and send the data to the server. The server analyzes the image to extract the book's title and edition information and provides a corresponding market value. Based on past transaction data, if it determines that summer is particularly suitable for selling, it will recommend this to the user. A list of reliable trading partners is also provided, allowing the user to choose the method that best suits their needs from the available options.

[0281] An example of a prompt message might be, "Please tell me the current market value and recommended selling time for the painting (artist name, title, year of creation) that you intend to sell."

[0282] Thus, the system of the present invention helps users efficiently and effectively understand the value of goods and conduct optimal buying and selling activities.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user uses the camera of the smartphone to take a video of the owned item. What is obtained as input is the captured digital image. The user selects this image with a dedicated application on the terminal and registers it together with the basic information about the item (e.g., item name, model number, manufacturing year). As output, the image of the item and the accompanying basic information are saved on the terminal as a dataset.

[0286] Step 2:

[0287] The terminal sends the registered image data and basic information of the item to the server. The input is the dataset saved earlier, and the output is the digital packet transferred to the server. The terminal uses a secure communication protocol via the Internet to maintain data integrity and confidentiality.

[0288] Step 3:

[0289] The server processes the received data and first inputs the image data into the generative AI model. As a specific operation, image analysis by a Convolutional Neural Network (CNN) is performed to extract the feature information of the item. The input is the image data of the item, and the output is the feature information of the item. Through this analysis, the conditions under which the item is concessioned and identification data (e.g., brand and model) are specified.

[0290] Step 4:

[0291] The server uses the extracted feature information to execute a query against the internal market transaction information database. The input is the feature information, and by performing a database search, the predicted market value of the item is obtained as output. The database records past and current market trends, and the value of the item is calculated based on this.

[0292] Step 5:

[0293] The server analyzes historical market trend data and seasonal characteristics to calculate the optimal selling time. The input is market transaction information and seasonal characteristics, and the output is the optimal selling time presented to the user. This allows the user to create a selling plan that takes price fluctuations into account.

[0294] Step 6:

[0295] The server collects reliable trading partner information and selects suitable buyers. Input is past evaluation and review information, and output is recommended trading partner information. Users can use this information to make informed decisions about which buyer will offer the most favorable terms.

[0296] (Application Example 1)

[0297] 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."

[0298] When individuals sell their personal belongings, accurately determining their market value, finding the optimal selling time, and discovering a trustworthy buyer are all challenging. Furthermore, understanding market trends and demand in real time and conducting transactions under the most favorable conditions is also a challenge. Moreover, there is a lack of readily available means for users to access this information.

[0299] 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.

[0300] In this invention, the server includes means for capturing digital images of items owned by an individual and registering them in an information processing device; means for analyzing the captured digital images using a machine learning algorithm and automatically extracting characteristic information of the items; and means for referring to transaction information in a database and predicting the current market value of the items based on the analyzed characteristic information. This allows individuals to understand the market value of their owned items in real time, easily find the optimal time to sell and a reliable buyer, and conduct transactions under the most favorable conditions.

[0301] "Means of taking digital images of personal belongings and registering them in an information processing device" refers to a method of taking images of personal belongings with a digital camera or similar device, and inputting and saving that image data into a system.

[0302] "A means of analyzing digitally captured images using machine learning algorithms and automatically extracting characteristic information of items" refers to a method that applies machine learning technology to analyze registered images and automatically identify the brand, model number, and other identifiers of an item.

[0303] "A means of predicting the current market value of an item based on analyzed characteristic information by referring to transaction information in a database" refers to a process for predicting the price of an identified item by utilizing past and present market transaction data in a collected database.

[0304] "A means of analyzing temporal market trends and cyclical demand to calculate and provide the optimal time to sell goods" refers to a method of analyzing market data to identify price fluctuations and demand peaks, and then determining the optimal timing for selling based on those insights.

[0305] "Means of selecting and presenting reliable selling institutions to users" refers to the process of identifying trustworthy vendors as suppliers of goods, taking into account evaluation data and reliability indicators, and providing users with a list of such vendors.

[0306] "Means of providing users with evaluation indicators and evaluation information of selling institutions" refers to a mechanism that presents reviews and evaluation scores of merchants for users' reference when selecting a selling destination.

[0307] "Means of providing an application program executable on a user's mobile information terminal and presenting market value information and selling destination information thereby" refers to a method of providing an application that operates on a mobile terminal such as a smartphone or tablet and displays price information and selling destination information.

[0308] The system for realizing this invention mainly uses a mobile information terminal such as a smartphone or tablet and a cloud server. The user takes a digital image of the owned item with a smartphone and transmits the image to the server through a dedicated application. The terminal is equipped with an interface for taking photos and inputting them into the application.

[0309] On the server, the backend operates using Flask built in Python, and the machine learning algorithm of TensorFlow analyzes the received image data. Through this analysis, the brand, model, and other characteristic information of the item are automatically extracted. Based on the characteristic information thus obtained, the server refers to the market transaction data accumulated in the database and executes a program for predicting the current market value.

[0310] Furthermore, the server analyzes past market trends and seasonal demand data, and calculates the most advantageous time to sell the item. In addition, it selects reliable selling institutions and provides a list based on their evaluation indicators to the user terminal. Based on this information, the user can determine the optimal timing and conditions for the transaction and proceed with the execution.

[0311] As a concrete example, suppose an individual wants to sell an old wristwatch. They take a picture of it and send it to the system. The system identifies the watch's model and brand and displays its market value. At the same time, it recommends that the best time to sell is before the next new model is released. In this way, users can easily sell their watches under better conditions.

[0312] An example of a prompt using a generative AI model is as follows: "Analyze the market value of this item and suggest the optimal time to sell it and a reliable buyer."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] Users use their mobile devices to take digital images of items they wish to sell and send the images, along with necessary basic information, to the server via a dedicated application. Input includes the product name, model number, and the captured image file. Output is a notification that the transmission to the server is complete.

[0316] Step 2:

[0317] The server begins processing the received image data and item information, analyzing the images using a machine learning algorithm powered by TensorFlow. This automatically extracts the brand name and model number from the input image. The output is item feature information.

[0318] Step 3:

[0319] The server calculates market value by referencing past transaction information in the database based on the generated feature information. The inputs used are feature information and transaction data, and by matching these, the server predicts the current market price. The output is the predicted market value.

[0320] Step 4:

[0321] The server uses market value results and analyzes past market trends and seasonal demand to calculate the optimal selling time. The input for data analysis includes market value and trend data, and the output is the recommended selling time.

[0322] Step 5:

[0323] The server selects reliable selling agencies based on evaluation metrics and creates a list of candidates. This process uses agency evaluation data as input and outputs a list of reliable agencies.

[0324] Step 6:

[0325] The server returns the final analysis results to the user's terminal, including market value, optimal selling time, and a list of reliable buyers. The input includes the output results from the previous stage, and the output is the display of information in the user's application.

[0326] Step 7:

[0327] The user makes a sale decision based on the information provided. The application then displays an interface where the user can select the next action. The input includes the user's selection, and the output is the selection of the next action.

[0328] 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.

[0329] This invention combines a system that appropriately evaluates the market value of personal belongings and recommends the optimal timing and trading partners for their sale with an emotion engine that recognizes and utilizes the user's emotions. This system mainly consists of a user terminal, a server, and an emotion recognition module.

[0330] Users take pictures of items they are considering selling using a smartphone or other device and input the information within the app. During the input process, the app captures the user's facial expressions and voice to recognize emotions. At this time, an emotion engine analyzes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, etc.) in real time. This information is reflected in the operation of the entire system and used to customize recommended information.

[0331] The terminal sends image data and information registered by the user to the server. The server applies a machine learning model and identifies the characteristics of the item through image analysis. Based on the extracted characteristic information, a process is carried out to predict the market value of the item by referring to market transaction data. Furthermore, the server analyzes past market trends to recommend the optimal time to sell. It also takes into account sentiment data recognized by the sentiment engine and presents the user with optimized buyer information and transaction conditions.

[0332] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the user takes a photo of the watch through the app, and the app detects a slight smile on their face during the photo shoot. This emotional state suggests the user has a positive view of the watch's market value and their expectations for selling it. The server incorporates this emotional information and recommends leading buyers while simultaneously emphasizing positive language to encourage immediate transactions.

[0333] This allows for a more personalized trading experience that takes into account the user's emotions, intention to sell, and level of trust. The system aims to improve the efficiency and comfort of the asset buying and selling process by utilizing user emotions.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] When a user takes pictures of items they are considering selling with their smartphone, they register the images through the application and input basic information about the items. During this process, the user's facial expressions and voice data are collected by the device.

[0337] Step 2:

[0338] The device uses an emotion engine to analyze the user's emotional state in real time from their facial expressions and voice data. The analysis results (for example, whether the user is satisfied or anxious) are digitized and sent to the server along with other data.

[0339] Step 3:

[0340] The server analyzes the received image data using a machine learning model to extract the characteristics of the items (brand name, model, condition, etc.). This characteristic information, combined with a market transaction database, serves as a basis for predicting the market value of the items.

[0341] Step 4:

[0342] The server analyzes past market trends and seasonal demand data to recommend the best time to sell items. It then considers user sentiment data and adjusts how the optimal timing is presented (e.g., using more positive content).

[0343] Step 5:

[0344] The server selects and presents reliable buyers to the user based on market data and sentiment information. This includes evaluation information of the buyers, and recommendation language is used that is tailored to the user's emotional state.

[0345] Step 6:

[0346] Based on the information provided by the user, decisions regarding the buyer and timing of the sale are made. The user's selection is registered in the system, and the transaction begins. During this process, if the user's feelings change, this is recognized again and reflected in the data.

[0347] Step 7:

[0348] After a transaction is completed, the server analyzes the user's sentiment data along with the entire transaction history, and uses this data to improve the system and enhance personalization in the future. This data forms the basis for improving the quality of service in subsequent transactions.

[0349] (Example 2)

[0350] 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".

[0351] Traditional asset trading systems made recommendations based solely on market data, without considering user emotions or subjective judgments. This resulted in insufficient consideration of user satisfaction when making trading decisions. Consequently, there was a risk of decreased user confidence in the recommendations provided and reduced satisfaction with trading results.

[0352] 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.

[0353] In this invention, the server includes means for capturing and registering images of items owned by an individual, means for analyzing the user's facial expressions and voice using emotion recognition technology to acquire emotion data, and means for customizing sales information taking the emotion data into consideration. This makes it possible to provide personalized sales recommendations based on the user's emotions.

[0354] "Images of items" refer to digital information that visually captures the items being considered for sale.

[0355] A "machine learning model" is a technology that includes algorithms for analyzing data such as images and identifying features and patterns based on that data.

[0356] "Market value" refers to the expected price or economic value of a particular item when it is traded in the market.

[0357] "Market trends" refer to market movements and general tendencies based on past trading data and supply and demand fluctuations.

[0358] "Emotion recognition technology" is a technology that identifies a person's emotional state from their facial expressions and voice.

[0359] "Emotional data" refers to information that expresses a user's emotions in a numerical or categorical form.

[0360] A "buyer" refers to a trading partner, such as a business or individual, who may potentially purchase the goods.

[0361] "Customization" is the process of optimizing and individualizing information and services based on specific criteria and conditions.

[0362] This invention is a system that appropriately evaluates the market value of personal belongings and recommends the optimal buying and selling timing and trading partners, incorporating technology to recognize and utilize user emotions. The system mainly consists of a user terminal, a server, and an emotion recognition module.

[0363] Users take pictures of items they are considering selling using a smartphone or other device, and input information about the items within the app. The device used here is a personal digital assistant (PDA) equipped with a camera and microphone for image processing and emotion recognition.

[0364] The terminal sends the input image data and item information to the server. The server analyzes the images using a machine learning model and extracts feature information about the items. This machine learning model includes a general-purpose neural network for image recognition. This feature information is used to predict the current market value of the items by referencing a market transaction database.

[0365] Furthermore, the terminal captures the user's facial expressions and voice through an emotion recognition module to obtain the user's emotional data. This emotional data reflects the user's expectations and level of trust regarding the transaction. This emotional data is considered by the server because it influences the selection of proposed buyers and the presentation of sale terms.

[0366] The server further analyzes past market trends and seasonal demand based on the acquired data, providing recommendations for the optimal time to sell items. In this way, users receive personalized selling suggestions based on emotion, allowing them to sell under the most favorable conditions.

[0367] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the emotion engine detects that the user is slightly relieved as they take photos of the watch. Based on this emotion data, the server prioritizes recommending favorable and trustworthy buyers and provides the user with information to encourage immediate transaction.

[0368] An example of a prompt to input into a generating AI model is: "Please describe a system that evaluates the market value of an item a user is considering selling, based on images and sentiment data related to the item, and recommends the best buyer and timing."

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user takes a picture of the item they are considering selling using their device's camera and enters detailed information about the item (brand, model, year of purchase, etc.) into the app. This data is temporarily stored on the device as the item's basic information. This information is then sent to the server in the next step.

[0372] Step 2:

[0373] The device captures the user's facial expressions and voice, and uses a built-in emotion recognition module to analyze the user's emotional data in real time. Inputs include camera video and audio data, and output is the analyzed emotional state (e.g., satisfaction, dissatisfaction, interest, etc.). This emotional data is stored as data indicating the user's attitude towards the transaction.

[0374] Step 3:

[0375] The device transmits collected image data, item information, and emotion data to the server. This data transmission is performed using a secure communication protocol, and the server receives and stores the data.

[0376] Step 4:

[0377] The server receives image data and analyzes the features of the objects using a machine learning model. Specifically, it performs image recognition using a neural network to extract feature information about the objects. The input is image data, and the output is a feature vector that identifies the objects.

[0378] Step 5:

[0379] The server uses the item's characteristic information to refer to a market transaction database and predict the item's market value. Using database queries and statistical models, the system takes the characteristic information as input and generates a predicted market price as output.

[0380] Step 6:

[0381] The server analyzes past market trends and proposes the optimal selling time. This analysis uses time-series data analysis and takes market fluctuation patterns into consideration. As a result, the server proposes a recommended selling time to the user.

[0382] Step 7:

[0383] Based on recognized sentiment data, the server presents the user with buyers and transaction terms optimized for them. This process takes sentiment data as input and outputs buyers and terms likely to be favorable to the user. If positive sentiment is detected, information encouraging immediate transactions is highlighted.

[0384] (Application Example 2)

[0385] 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."

[0386] When individuals sell their belongings, they need optimal market valuation, the right timing for the sale, and reliable buyers. However, providing personalized selling recommendations that take user emotions into consideration is difficult. Furthermore, standard selling processes often fail to adequately reflect user intentions and feelings, resulting in lower satisfaction.

[0387] 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.

[0388] This invention includes a server that includes means for acquiring and registering visual data of items owned by an individual; means for analyzing the acquired visual data using a machine learning model and extracting characteristic information of the items; means for referencing market transaction information and evaluating the current market value of the items based on the analyzed characteristic information; means for analyzing past market trends and demand forecasts and recommending the optimal time to sell the items; and means for evaluating the user's emotional state in real time using an emotion analysis engine and optimizing the user's intention to sell the items. This makes it possible to provide personalized recommendations based on the user's emotions and improve satisfaction with the selling process.

[0389] "Visual data of personal belongings" refers to image and video data acquired to identify personal belongings owned by an individual.

[0390] A "machine learning model" is a computational algorithm that extracts features from data and learns patterns to make future predictions and classifications.

[0391] "Characteristic information" refers to information that indicates the unique characteristics of an item, and it serves as basic data for evaluating its market value.

[0392] "Market transaction information" refers to data including the history of buying and selling goods in the market and price trends, and is used to evaluate the market value of goods.

[0393] "Past market trends" refers to data that shows the history of price fluctuations and demand in the market in the past.

[0394] "Demand forecasting" refers to the results of an analysis aimed at predicting future trends in market demand for goods.

[0395] "Optimal time" refers to the period when it is believed that the greatest profit can be obtained when selling an item.

[0396] An "emotion analysis engine" is a software system that evaluates a user's emotional state in real time by analyzing their facial expressions and voice.

[0397] "User emotional state" refers to information that indicates the psychological state of the user that influences their decision to sell an item.

[0398] "Personalized recommendations" refer to providing information and suggestions that are customized according to the individual user's characteristics and emotions.

[0399] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server works in conjunction with a terminal that has the function of acquiring visual data of items owned by an individual, and extracts characteristic information of the items using a machine learning model based on that visual data. Specifically, the terminal uses a camera device such as a smartphone to take visual data of items that the user is considering selling, and transmits this data to the server.

[0400] The server uses Microsoft Azure's Face API to perform facial recognition and emotion analysis on the received visual data. It also uses Google Cloud's Speech-to-Text API to convert speech input into text, and uses this information to evaluate the user's emotional state. The extracted characteristic information is stored in Amazon Web Services (AWS) DynamoDB and cross-referenced with market transaction data.

[0401] Market transaction information is analyzed using historical market trends and demand forecast data collected by the server and used to calculate the optimal selling time. Based on the user's emotional state revealed by sentiment analysis, the server suggests personalized buyers and timing. For example, if a user shows a strong interest in a luxury watch, the server will notify the user's terminal in real time of the best buyers and recommendations related to that watch.

[0402] As a specific use case, when a user tries to assess the value of household items using their smartphone, the system performs emotion analysis when the camera takes a picture of an item. If positive emotions are detected, it immediately highlights the item as a recommendation to sell. Prompt messages such as "Analyze the facial expressions the user makes while browsing a product page and notify them of special offers related to that product if they show interest" are used as a reference to enrich the user experience.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The device uses its camera to photograph items the user is considering selling, acquiring visual data. This visual data is sent to the server as input from the user. The output is transferred to the server as image data.

[0406] Step 2:

[0407] The server analyzes the received image data using a machine learning model to extract characteristic information about the items. The input is image data, and the output is characteristic information about the items derived from the images. This process uses a characteristic extraction algorithm.

[0408] Step 3:

[0409] The server evaluates the market value of an item by referencing market transaction information based on its characteristic data. The input is the characteristic data, and the output is the evaluated market value. This process utilizes data matching with historical transaction data and a valuation model.

[0410] Step 4:

[0411] The server analyzes historical market trends and demand forecast data to calculate the optimal selling time. The input is market value information, and the output is a recommendation for the optimal selling time. This analysis uses a trend analysis algorithm.

[0412] Step 5:

[0413] The device uses the user's camera and microphone to acquire facial and audio data, and an emotion analysis engine evaluates the user's emotional state. The input is the user's emotional data, and the output is the analyzed emotional state. Emotion recognition technology is used in this process.

[0414] Step 6:

[0415] The server suggests personalized buyers and selling conditions to the user based on their emotional state and market value information. The input is emotional state and market value information, and the output is individually customized recommendations. This step involves individual optimization by an AI model.

[0416] Step 7:

[0417] The user receives recommendations from the server on their device and selects the appropriate action. This results in a personalized selling experience. The input is the recommendations from the server, and the output is the user's decision.

[0418] 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.

[0419] 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 those described above. 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 shown 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] The present invention provides information to understand the market value of personal belongings and to optimize their buying and selling. This system primarily consists of a process of collecting and analyzing information about items and providing recommendation information through communication between a terminal and a server.

[0435] Users take photos of their belongings with their smartphone or other camera and register the images on their device. It is recommended that users enter basic information about the item (e.g., product name, model number, and year of manufacture) during registration. This allows users to directly incorporate item data into the system in digital format from the initial stages.

[0436] The terminal transfers the registered images and information to the server. The server first analyzes the received image data using a machine learning model to identify the characteristics of the item. For example, if it is a luxury watch, it extracts the brand, model name, serial number, etc. Based on this information, the server matches it with market transaction data to predict the current market value of the item.

[0437] Furthermore, the server recommends the optimal selling time to the user based on past market trends and seasonal demand. This involves analyzing historical transaction data to identify price fluctuation cycles and demand timing.

[0438] In addition, the server selects and presents information on reliable buyers, such as highly-rated buyers. This information serves as a guide for users to conduct transactions under the most favorable conditions. Users can compare the conditions and ratings of the presented candidates and make the most appropriate choice.

[0439] For example, when a user wants to sell a luxury car, they take pictures of the vehicle with their smartphone and register them in the application. The server analyzes the images, refers to the vehicle model and current market conditions, and calculates a predicted market price. Furthermore, based on past price trends of similar vehicles, it can recommend that the optimal time to sell would be a few months from now. Based on this information, the user can select the company offering the best terms from the listed buyers and proceed with negotiations.

[0440] As described above, the system of the present invention helps users accurately grasp the market value of their owned items and conduct buying and selling activities efficiently and effectively.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] Users use their smartphones to take pictures of the items they are considering selling and enter item information through the app. This information includes product name, model, and year of manufacture.

[0444] Step 2:

[0445] The device transfers image data and related information provided by the user to the server. The data is encrypted and transmitted securely.

[0446] Step 3:

[0447] The server analyzes the received images using a machine learning model to extract characteristic information about the items. For example, it identifies the brand and model of a watch, or the manufacturer and model number of a car.

[0448] Step 4:

[0449] The server references a market transaction database and predicts the current market value of an item based on extracted characteristic information. This utilizes price data from similar past transactions.

[0450] Step 5:

[0451] The server analyzes past market trends and seasonal demand to calculate the optimal time to sell. The analysis is based on past price fluctuation patterns.

[0452] Step 6:

[0453] The server selects and presents reliable buyers to the user based on their geographical location and the type of items they sell. Evaluation information for the buyers is also provided.

[0454] Step 7:

[0455] Based on the information provided by the user, the buyer and timing of the sale are determined. Details of the selected company and sale timing can be viewed through the app.

[0456] (Example 1)

[0457] 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."

[0458] This solution addresses the challenge of quickly and accurately determining the fair market value of personal belongings and finding the optimal time and reliable buyer for sale.

[0459] 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.

[0460] In this invention, the server includes means for analyzing images acquired using an artificial intelligence model and extracting characteristic information of an item; means for referring to market transaction information and predicting the current market value of the item based on the analyzed characteristic information; and means for analyzing past market trends and timing characteristics and recommending the optimal time to sell the item. This makes it possible for individuals to accurately grasp the market value of items they own and to conduct efficient buying and selling transactions.

[0461] An "individual" is a sole natural person who owns a particular item and intends to buy or sell it.

[0462] "Goods" refer to specific products or possessions owned by an individual that are subject to buying and selling.

[0463] "Video" refers to digital images or video files that visually record objects.

[0464] "Registration" refers to the process of inputting acquired video footage and related information into the system and saving it to the database.

[0465] An "artificial intelligence model" is a computational method that incorporates machine learning algorithms for data analysis and prediction.

[0466] "Characteristic information" refers to specific attributes and data points that are useful for identifying and classifying items.

[0467] "Market transaction information" refers to data on past and present market buying and selling prices and demand trends.

[0468] "Market value" refers to the expected buying and selling price or valuation of an item in the current market environment.

[0469] "Market trends" refer to the tendencies of supply and demand fluctuations and price changes in the market over time.

[0470] "Periodic characteristics" refer to the characteristics of market demand and price fluctuations during a specific period.

[0471] A "business partner" refers to a reliable company or entity in the buying and selling of goods.

[0472] "Evaluation information" refers to information based on opinions and ratings from other users regarding a business partner.

[0473] The system in this invention aims to quickly and accurately determine the market value of personal belongings and to find the optimal time for sale and a reliable buyer. This system operates using terminals and servers.

[0474] The user first takes a photograph of an item they own using a mobile device such as a smartphone. The captured image is registered using a dedicated application on the device. This application provides an interface that allows the user to input basic information about the item (e.g., item name, model number, year of manufacture) along with the image.

[0475] The terminal transmits registered video and information to the server. This transmission uses a secure communication method via the internet. The server analyzes the received video data using generative AI models such as Convolutional Neural Networks (CNNs) to extract characteristic information about the objects. This allows the server to recognize what the objects are and how they should be classified.

[0476] Next, the server references the characteristic information and compares it with a large-scale market transaction information database it maintains internally. This database stores information on past transaction prices and market supply and demand trends. Based on this data, the server predicts the market value of the item and provides it to the user.

[0477] Furthermore, the server has the capability to analyze historical market trend data and seasonal characteristics. This allows it to identify the optimal selling time and recommend it to the user. It also uses a reliability-based selection algorithm to present highly-rated trading partner information. This information includes evaluations and reviews from past users.

[0478] For example, if a user wants to sell a rare book, they capture an image of the book on their device and send the data to the server. The server analyzes the image to extract the book's title and edition information and provides a corresponding market value. Based on past transaction data, if it determines that summer is particularly suitable for selling, it will recommend this to the user. A list of reliable trading partners is also provided, allowing the user to choose the method that best suits their needs from the available options.

[0479] An example of a prompt message might be, "Please tell me the current market value and recommended selling time for the painting (artist name, title, year of creation) that you intend to sell."

[0480] Thus, the system of the present invention helps users efficiently and effectively understand the value of goods and conduct optimal buying and selling activities.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] The user uses their smartphone camera to take a picture of an item they own. The input is the captured digital image. The user selects this image in a dedicated application on their device and registers it along with basic information about the item (e.g., item name, model number, year of manufacture). The output is a dataset containing the item's image and associated basic information, which is saved on the device.

[0484] Step 2:

[0485] The terminal transmits image data and basic information of registered items to the server. The input is the previously saved dataset, and the output is the digital packet it is transferred to the server. The terminal uses a secure communication protocol over the internet to maintain data integrity and confidentiality.

[0486] Step 3:

[0487] The server processes the received data and first inputs the image data into an AI model. Specifically, it performs image analysis using a Convolutional Neural Network (CNN) to extract feature information of the items. The input is image data of the items, and the output is feature information of the items. This analysis identifies the conditions under which an item is sold and the identification data (e.g., brand or model).

[0488] Step 4:

[0489] The server uses the extracted feature information to query its internal market transaction information database. The input is feature information, and by performing a database search, the predicted market value of the item is obtained as output. The database records past and present market trends, and the value of the item is calculated based on this.

[0490] Step 5:

[0491] The server analyzes historical market trend data and seasonal characteristics to calculate the optimal selling time. The input is market transaction information and seasonal characteristics, and the output is the optimal selling time presented to the user. This allows the user to create a selling plan that takes price fluctuations into account.

[0492] Step 6:

[0493] The server collects reliable trading partner information and selects suitable buyers. Input is past evaluation and review information, and output is recommended trading partner information. Users can use this information to make informed decisions about which buyer will offer the most favorable terms.

[0494] (Application Example 1)

[0495] 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."

[0496] When individuals sell their personal belongings, accurately determining their market value, finding the optimal selling time, and discovering a trustworthy buyer are all challenging. Furthermore, understanding market trends and demand in real time and conducting transactions under the most favorable conditions is also a challenge. Moreover, there is a lack of readily available means for users to access this information.

[0497] 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.

[0498] In this invention, the server includes means for capturing digital images of items owned by an individual and registering them in an information processing device; means for analyzing the captured digital images using a machine learning algorithm and automatically extracting characteristic information of the items; and means for referring to transaction information in a database and predicting the current market value of the items based on the analyzed characteristic information. This allows individuals to understand the market value of their owned items in real time, easily find the optimal time to sell and a reliable buyer, and conduct transactions under the most favorable conditions.

[0499] "Means of taking digital images of personal belongings and registering them in an information processing device" refers to a method of taking images of personal belongings with a digital camera or similar device, and inputting and saving that image data into a system.

[0500] "A means of analyzing digitally captured images using machine learning algorithms and automatically extracting characteristic information of items" refers to a method that applies machine learning technology to analyze registered images and automatically identify the brand, model number, and other identifiers of an item.

[0501] "A means of predicting the current market value of an item based on analyzed characteristic information by referring to transaction information in a database" refers to a process for predicting the price of an identified item by utilizing past and present market transaction data in a collected database.

[0502] "A means of analyzing temporal market trends and cyclical demand to calculate and provide the optimal time to sell goods" refers to a method of analyzing market data to identify price fluctuations and demand peaks, and then determining the optimal timing for selling based on those insights.

[0503] "Means of selecting and presenting reliable selling institutions to users" refers to the process of identifying trustworthy vendors as suppliers of goods, taking into account evaluation data and reliability indicators, and providing users with a list of such vendors.

[0504] "Means of providing users with evaluation indicators and evaluation information for selling institutions" refers to a system that presents reviews and evaluation scores of companies that users can use as a reference when selecting a buyer.

[0505] "A means of providing an application program that can be executed on a user's mobile device, thereby presenting market value information and buyer information" refers to a method of providing an application that operates on a mobile device such as a smartphone or tablet and displays price information and buyer information.

[0506] The system for realizing this invention primarily uses mobile information terminals such as smartphones and tablets, and a cloud server. Users take digital images of their belongings with their smartphones and send those images to the server through a dedicated application. The terminals are equipped with an interface for taking photos and inputting them into the application.

[0507] On the server, the backend runs using Flask, built with Python, and TensorFlow machine learning algorithms analyze the received image data. This analysis automatically extracts the brand, model, and other feature information of the items. Based on this feature information, the server references market transaction data stored in a database and runs a program to predict the current market value.

[0508] Furthermore, the server analyzes past market trends and seasonal demand data to calculate the most profitable time to sell goods. It also selects reliable selling agencies and provides users with a list of these agencies based on evaluation criteria. Based on this information, users can determine the optimal timing and conditions for their transactions and then execute them.

[0509] As a concrete example, suppose an individual wants to sell an old wristwatch. They take a picture of it and send it to the system. The system identifies the watch's model and brand and displays its market value. At the same time, it recommends that the best time to sell is before the next new model is released. In this way, users can easily sell their watches under better conditions.

[0510] An example of a prompt using a generative AI model is as follows: "Analyze the market value of this item and suggest the optimal time to sell it and a reliable buyer."

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] Users use their mobile devices to take digital images of items they wish to sell and send the images, along with necessary basic information, to the server via a dedicated application. Input includes the product name, model number, and the captured image file. Output is a notification that the transmission to the server is complete.

[0514] Step 2:

[0515] The server begins processing the received image data and item information, analyzing the images using a machine learning algorithm powered by TensorFlow. This automatically extracts the brand name and model number from the input image. The output is item feature information.

[0516] Step 3:

[0517] The server calculates market value by referencing past transaction information in the database based on the generated feature information. The inputs used are feature information and transaction data, and by matching these, the server predicts the current market price. The output is the predicted market value.

[0518] Step 4:

[0519] The server uses market value results and analyzes past market trends and seasonal demand to calculate the optimal selling time. The input for data analysis includes market value and trend data, and the output is the recommended selling time.

[0520] Step 5:

[0521] The server selects reliable selling agencies based on evaluation metrics and creates a list of candidates. This process uses agency evaluation data as input and outputs a list of reliable agencies.

[0522] Step 6:

[0523] The server returns the final analysis results to the user's terminal, including market value, optimal selling time, and a list of reliable buyers. The input includes the output results from the previous stage, and the output is the display of information in the user's application.

[0524] Step 7:

[0525] The user makes a sale decision based on the information provided. The application then displays an interface where the user can select the next action. The input includes the user's selection, and the output is the selection of the next action.

[0526] 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.

[0527] This invention combines a system that appropriately evaluates the market value of personal belongings and recommends the optimal timing and trading partners for their sale with an emotion engine that recognizes and utilizes the user's emotions. This system mainly consists of a user terminal, a server, and an emotion recognition module.

[0528] Users take pictures of items they are considering selling using a smartphone or other device and input the information within the app. During the input process, the app captures the user's facial expressions and voice to recognize emotions. At this time, an emotion engine analyzes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, etc.) in real time. This information is reflected in the operation of the entire system and used to customize recommended information.

[0529] The terminal sends image data and information registered by the user to the server. The server applies a machine learning model and identifies the characteristics of the item through image analysis. Based on the extracted characteristic information, a process is carried out to predict the market value of the item by referring to market transaction data. Furthermore, the server analyzes past market trends to recommend the optimal time to sell. It also takes into account sentiment data recognized by the sentiment engine and presents the user with optimized buyer information and transaction conditions.

[0530] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the user takes a photo of the watch through the app, and the app detects a slight smile on their face during the photo shoot. This emotional state suggests the user has a positive view of the watch's market value and their expectations for selling it. The server incorporates this emotional information and recommends leading buyers while simultaneously emphasizing positive language to encourage immediate transactions.

[0531] This allows for a more personalized trading experience that takes into account the user's emotions, intention to sell, and level of trust. The system aims to improve the efficiency and comfort of the asset buying and selling process by utilizing user emotions.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] When a user takes pictures of items they are considering selling with their smartphone, they register the images through the application and input basic information about the items. During this process, the user's facial expressions and voice data are collected by the device.

[0535] Step 2:

[0536] The device uses an emotion engine to analyze the user's emotional state in real time from their facial expressions and voice data. The analysis results (for example, whether the user is satisfied or anxious) are digitized and sent to the server along with other data.

[0537] Step 3:

[0538] The server analyzes the received image data using a machine learning model to extract the characteristics of the items (brand name, model, condition, etc.). This characteristic information, combined with a market transaction database, serves as a basis for predicting the market value of the items.

[0539] Step 4:

[0540] The server analyzes past market trends and seasonal demand data to recommend the best time to sell items. It then considers user sentiment data and adjusts how the optimal timing is presented (e.g., using more positive content).

[0541] Step 5:

[0542] The server selects and presents reliable buyers to the user based on market data and sentiment information. This includes evaluation information of the buyers, and recommendation language is used that is tailored to the user's emotional state.

[0543] Step 6:

[0544] Based on the information provided by the user, decisions regarding the buyer and timing of the sale are made. The user's selection is registered in the system, and the transaction begins. During this process, if the user's feelings change, this is recognized again and reflected in the data.

[0545] Step 7:

[0546] After a transaction is completed, the server analyzes the user's sentiment data along with the entire transaction history, and uses this data to improve the system and enhance personalization in the future. This data forms the basis for improving the quality of service in subsequent transactions.

[0547] (Example 2)

[0548] 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."

[0549] Traditional asset trading systems made recommendations based solely on market data, without considering user emotions or subjective judgments. This resulted in insufficient consideration of user satisfaction when making trading decisions. Consequently, there was a risk of decreased user confidence in the recommendations provided and reduced satisfaction with trading results.

[0550] 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.

[0551] In this invention, the server includes means for capturing and registering images of items owned by an individual, means for analyzing the user's facial expressions and voice using emotion recognition technology to acquire emotion data, and means for customizing sales information taking the emotion data into consideration. This makes it possible to provide personalized sales recommendations based on the user's emotions.

[0552] "Images of items" refer to digital information that visually captures the items being considered for sale.

[0553] A "machine learning model" is a technology that includes algorithms for analyzing data such as images and identifying features and patterns based on that data.

[0554] "Market value" refers to the expected price or economic value of a particular item when it is traded in the market.

[0555] "Market trends" refer to market movements and general tendencies based on past trading data and supply and demand fluctuations.

[0556] "Emotion recognition technology" is a technology that identifies a person's emotional state from their facial expressions and voice.

[0557] "Emotional data" refers to information that expresses a user's emotions in a numerical or categorical form.

[0558] A "buyer" refers to a trading partner, such as a business or individual, who may potentially purchase the goods.

[0559] "Customization" is the process of optimizing and individualizing information and services based on specific criteria and conditions.

[0560] This invention is a system that appropriately evaluates the market value of personal belongings and recommends the optimal buying and selling timing and trading partners, incorporating technology to recognize and utilize user emotions. The system mainly consists of a user terminal, a server, and an emotion recognition module.

[0561] Users take pictures of items they are considering selling using a smartphone or other device, and input information about the items within the app. The device used here is a personal digital assistant (PDA) equipped with a camera and microphone for image processing and emotion recognition.

[0562] The terminal sends the input image data and item information to the server. The server analyzes the images using a machine learning model and extracts feature information about the items. This machine learning model includes a general-purpose neural network for image recognition. This feature information is used to predict the current market value of the items by referencing a market transaction database.

[0563] Furthermore, the terminal captures the user's facial expressions and voice through an emotion recognition module to obtain the user's emotional data. This emotional data reflects the user's expectations and level of trust regarding the transaction. This emotional data is considered by the server because it influences the selection of proposed buyers and the presentation of sale terms.

[0564] The server further analyzes past market trends and seasonal demand based on the acquired data, providing recommendations for the optimal time to sell items. In this way, users receive personalized selling suggestions based on emotion, allowing them to sell under the most favorable conditions.

[0565] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the emotion engine detects that the user is slightly relieved as they take photos of the watch. Based on this emotion data, the server prioritizes recommending favorable and trustworthy buyers and provides the user with information to encourage immediate transaction.

[0566] An example of a prompt to input into a generating AI model is: "Please describe a system that evaluates the market value of an item a user is considering selling, based on images and sentiment data related to the item, and recommends the best buyer and timing."

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The user takes a picture of the item they are considering selling using their device's camera and enters detailed information about the item (brand, model, year of purchase, etc.) into the app. This data is temporarily stored on the device as the item's basic information. This information is then sent to the server in the next step.

[0570] Step 2:

[0571] The device captures the user's facial expressions and voice, and uses a built-in emotion recognition module to analyze the user's emotional data in real time. Inputs include camera video and audio data, and output is the analyzed emotional state (e.g., satisfaction, dissatisfaction, interest, etc.). This emotional data is stored as data indicating the user's attitude towards the transaction.

[0572] Step 3:

[0573] The device transmits collected image data, item information, and emotion data to the server. This data transmission is performed using a secure communication protocol, and the server receives and stores the data.

[0574] Step 4:

[0575] The server receives image data and analyzes the features of the objects using a machine learning model. Specifically, it performs image recognition using a neural network to extract feature information about the objects. The input is image data, and the output is a feature vector that identifies the objects.

[0576] Step 5:

[0577] The server uses the item's characteristic information to refer to a market transaction database and predict the item's market value. Using database queries and statistical models, the system takes the characteristic information as input and generates a predicted market price as output.

[0578] Step 6:

[0579] The server analyzes past market trends and proposes the optimal selling time. This analysis uses time-series data analysis and takes market fluctuation patterns into consideration. As a result, the server proposes a recommended selling time to the user.

[0580] Step 7:

[0581] Based on recognized sentiment data, the server presents the user with buyers and transaction terms optimized for them. This process takes sentiment data as input and outputs buyers and terms likely to be favorable to the user. If positive sentiment is detected, information encouraging immediate transactions is highlighted.

[0582] (Application Example 2)

[0583] 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."

[0584] When individuals sell their belongings, they need optimal market valuation, the right timing for the sale, and reliable buyers. However, providing personalized selling recommendations that take user emotions into consideration is difficult. Furthermore, standard selling processes often fail to adequately reflect user intentions and feelings, resulting in lower satisfaction.

[0585] 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.

[0586] This invention includes a server that includes means for acquiring and registering visual data of items owned by an individual; means for analyzing the acquired visual data using a machine learning model and extracting characteristic information of the items; means for referencing market transaction information and evaluating the current market value of the items based on the analyzed characteristic information; means for analyzing past market trends and demand forecasts and recommending the optimal time to sell the items; and means for evaluating the user's emotional state in real time using an emotion analysis engine and optimizing the user's intention to sell the items. This makes it possible to provide personalized recommendations based on the user's emotions and improve satisfaction with the selling process.

[0587] "Visual data of personal belongings" refers to image and video data acquired to identify personal belongings owned by an individual.

[0588] A "machine learning model" is a computational algorithm that extracts features from data and learns patterns to make future predictions and classifications.

[0589] "Characteristic information" refers to information that indicates the unique characteristics of an item, and it serves as basic data for evaluating its market value.

[0590] "Market transaction information" refers to data including the history of buying and selling goods in the market and price trends, and is used to evaluate the market value of goods.

[0591] "Past market trends" refers to data that shows the history of price fluctuations and demand in the market in the past.

[0592] "Demand forecasting" refers to the results of an analysis aimed at predicting future trends in market demand for goods.

[0593] "Optimal time" refers to the period when it is believed that the greatest profit can be obtained when selling an item.

[0594] An "emotion analysis engine" is a software system that evaluates a user's emotional state in real time by analyzing their facial expressions and voice.

[0595] "User emotional state" refers to information that indicates the psychological state of the user that influences their decision to sell an item.

[0596] "Personalized recommendations" refer to providing information and suggestions that are customized according to the individual user's characteristics and emotions.

[0597] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server works in conjunction with a terminal that has the function of acquiring visual data of items owned by an individual, and extracts characteristic information of the items using a machine learning model based on that visual data. Specifically, the terminal uses a camera device such as a smartphone to take visual data of items that the user is considering selling, and transmits this data to the server.

[0598] The server uses Microsoft Azure's Face API to perform facial recognition and emotion analysis on the received visual data. It also uses Google Cloud's Speech-to-Text API to convert speech input into text, and uses this information to evaluate the user's emotional state. The extracted characteristic information is stored in Amazon Web Services (AWS) DynamoDB and cross-referenced with market transaction data.

[0599] Market transaction information is analyzed using historical market trends and demand forecast data collected by the server and used to calculate the optimal selling time. Based on the user's emotional state revealed by sentiment analysis, the server suggests personalized buyers and timing. For example, if a user shows a strong interest in a luxury watch, the server will notify the user's terminal in real time of the best buyers and recommendations related to that watch.

[0600] As a specific use case, when a user tries to assess the value of household items using their smartphone, the system performs emotion analysis when the camera takes a picture of an item. If positive emotions are detected, it immediately highlights the item as a recommendation to sell. Prompt messages such as "Analyze the facial expressions the user makes while browsing a product page and notify them of special offers related to that product if they show interest" are used as a reference to enrich the user experience.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The device uses its camera to photograph items the user is considering selling, acquiring visual data. This visual data is sent to the server as input from the user. The output is transferred to the server as image data.

[0604] Step 2:

[0605] The server analyzes the received image data using a machine learning model to extract characteristic information about the items. The input is image data, and the output is characteristic information about the items derived from the images. This process uses a characteristic extraction algorithm.

[0606] Step 3:

[0607] The server evaluates the market value of an item by referencing market transaction information based on its characteristic data. The input is the characteristic data, and the output is the evaluated market value. This process utilizes data matching with historical transaction data and a valuation model.

[0608] Step 4:

[0609] The server analyzes historical market trends and demand forecast data to calculate the optimal selling time. The input is market value information, and the output is a recommendation for the optimal selling time. This analysis uses a trend analysis algorithm.

[0610] Step 5:

[0611] The device uses the user's camera and microphone to acquire facial and audio data, and an emotion analysis engine evaluates the user's emotional state. The input is the user's emotional data, and the output is the analyzed emotional state. Emotion recognition technology is used in this process.

[0612] Step 6:

[0613] The server suggests personalized buyers and selling conditions to the user based on their emotional state and market value information. The input is emotional state and market value information, and the output is individually customized recommendations. This step involves individual optimization by an AI model.

[0614] Step 7:

[0615] The user receives recommendations from the server on their device and selects the appropriate action. This results in a personalized selling experience. The input is the recommendations from the server, and the output is the user's decision.

[0616] 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.

[0617] 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 those described above. 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 shown 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.

[0618] 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.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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".

[0633] The present invention provides information to understand the market value of personal belongings and to optimize their buying and selling. This system primarily consists of a process of collecting and analyzing information about items and providing recommendation information through communication between a terminal and a server.

[0634] Users take photos of their belongings with their smartphone or other camera and register the images on their device. It is recommended that users enter basic information about the item (e.g., product name, model number, and year of manufacture) during registration. This allows users to directly incorporate item data into the system in digital format from the initial stages.

[0635] The terminal transfers the registered images and information to the server. The server first analyzes the received image data using a machine learning model to identify the characteristics of the item. For example, if it is a luxury watch, it extracts the brand, model name, serial number, etc. Based on this information, the server matches it with market transaction data to predict the current market value of the item.

[0636] Furthermore, the server recommends the optimal selling time to the user based on past market trends and seasonal demand. This involves analyzing historical transaction data to identify price fluctuation cycles and demand timing.

[0637] In addition, the server selects and presents information on reliable buyers, such as highly-rated buyers. This information serves as a guide for users to conduct transactions under the most favorable conditions. Users can compare the conditions and ratings of the presented candidates and make the most appropriate choice.

[0638] For example, when a user wants to sell a luxury car, they take pictures of the vehicle with their smartphone and register them in the application. The server analyzes the images, refers to the vehicle model and current market conditions, and calculates a predicted market price. Furthermore, based on past price trends of similar vehicles, it can recommend that the optimal time to sell would be a few months from now. Based on this information, the user can select the company offering the best terms from the listed buyers and proceed with negotiations.

[0639] As described above, the system of the present invention helps users accurately grasp the market value of their owned items and conduct buying and selling activities efficiently and effectively.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] Users use their smartphones to take pictures of the items they are considering selling and enter item information through the app. This information includes product name, model, and year of manufacture.

[0643] Step 2:

[0644] The device transfers image data and related information provided by the user to the server. The data is encrypted and transmitted securely.

[0645] Step 3:

[0646] The server analyzes the received images using a machine learning model to extract characteristic information about the items. For example, it identifies the brand and model of a watch, or the manufacturer and model number of a car.

[0647] Step 4:

[0648] The server references a market transaction database and predicts the current market value of an item based on extracted characteristic information. This utilizes price data from similar past transactions.

[0649] Step 5:

[0650] The server analyzes past market trends and seasonal demand to calculate the optimal time to sell. The analysis is based on past price fluctuation patterns.

[0651] Step 6:

[0652] The server selects and presents reliable buyers to the user based on their geographical location and the type of items they sell. Evaluation information for the buyers is also provided.

[0653] Step 7:

[0654] Based on the information provided by the user, the buyer and timing of the sale are determined. Details of the selected company and sale timing can be viewed through the app.

[0655] (Example 1)

[0656] 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".

[0657] This solution addresses the challenge of quickly and accurately determining the fair market value of personal belongings and finding the optimal time and reliable buyer for sale.

[0658] 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.

[0659] In this invention, the server includes means for analyzing images acquired using an artificial intelligence model and extracting characteristic information of an item; means for referring to market transaction information and predicting the current market value of the item based on the analyzed characteristic information; and means for analyzing past market trends and timing characteristics and recommending the optimal time to sell the item. This makes it possible for individuals to accurately grasp the market value of items they own and to conduct efficient buying and selling transactions.

[0660] An "individual" is a sole natural person who owns a particular item and intends to buy or sell it.

[0661] "Goods" refer to specific products or possessions owned by an individual that are subject to buying and selling.

[0662] "Video" refers to digital images or video files that visually record objects.

[0663] "Registration" refers to the process of inputting acquired video footage and related information into the system and saving it to the database.

[0664] An "artificial intelligence model" is a computational method that incorporates machine learning algorithms for data analysis and prediction.

[0665] "Characteristic information" refers to specific attributes and data points that are useful for identifying and classifying items.

[0666] "Market transaction information" refers to data on past and present market buying and selling prices and demand trends.

[0667] "Market value" refers to the expected buying and selling price or valuation of an item in the current market environment.

[0668] "Market trends" refer to the tendencies of supply and demand fluctuations and price changes in the market over time.

[0669] "Periodic characteristics" refer to the characteristics of market demand and price fluctuations during a specific period.

[0670] A "business partner" refers to a reliable company or entity in the buying and selling of goods.

[0671] "Evaluation information" refers to information based on opinions and ratings from other users regarding a business partner.

[0672] The system in this invention aims to quickly and accurately determine the market value of personal belongings and to find the optimal time for sale and a reliable buyer. This system operates using terminals and servers.

[0673] The user first takes a photograph of an item they own using a mobile device such as a smartphone. The captured image is registered using a dedicated application on the device. This application provides an interface that allows the user to input basic information about the item (e.g., item name, model number, year of manufacture) along with the image.

[0674] The terminal transmits registered video and information to the server. This transmission uses a secure communication method via the internet. The server analyzes the received video data using generative AI models such as Convolutional Neural Networks (CNNs) to extract characteristic information about the objects. This allows the server to recognize what the objects are and how they should be classified.

[0675] Next, the server references the characteristic information and compares it with a large-scale market transaction information database it maintains internally. This database stores information on past transaction prices and market supply and demand trends. Based on this data, the server predicts the market value of the item and provides it to the user.

[0676] Furthermore, the server has the capability to analyze historical market trend data and seasonal characteristics. This allows it to identify the optimal selling time and recommend it to the user. It also uses a reliability-based selection algorithm to present highly-rated trading partner information. This information includes evaluations and reviews from past users.

[0677] For example, if a user wants to sell a rare book, they capture an image of the book on their device and send the data to the server. The server analyzes the image to extract the book's title and edition information and provides a corresponding market value. Based on past transaction data, if it determines that summer is particularly suitable for selling, it will recommend this to the user. A list of reliable trading partners is also provided, allowing the user to choose the method that best suits their needs from the available options.

[0678] An example of a prompt message might be, "Please tell me the current market value and recommended selling time for the painting (artist name, title, year of creation) that you intend to sell."

[0679] Thus, the system of the present invention helps users efficiently and effectively understand the value of goods and conduct optimal buying and selling activities.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The user uses their smartphone camera to take a picture of an item they own. The input is the captured digital image. The user selects this image in a dedicated application on their device and registers it along with basic information about the item (e.g., item name, model number, year of manufacture). The output is a dataset containing the item's image and associated basic information, which is saved on the device.

[0683] Step 2:

[0684] The terminal transmits image data and basic information of registered items to the server. The input is the previously saved dataset, and the output is the digital packet it is transferred to the server. The terminal uses a secure communication protocol over the internet to maintain data integrity and confidentiality.

[0685] Step 3:

[0686] The server processes the received data and first inputs the image data into an AI model. Specifically, it performs image analysis using a Convolutional Neural Network (CNN) to extract feature information of the items. The input is image data of the items, and the output is feature information of the items. This analysis identifies the conditions under which an item is sold and the identification data (e.g., brand or model).

[0687] Step 4:

[0688] The server uses the extracted feature information to query its internal market transaction information database. The input is feature information, and by performing a database search, the predicted market value of the item is obtained as output. The database records past and present market trends, and the value of the item is calculated based on this.

[0689] Step 5:

[0690] The server analyzes historical market trend data and seasonal characteristics to calculate the optimal selling time. The input is market transaction information and seasonal characteristics, and the output is the optimal selling time presented to the user. This allows the user to create a selling plan that takes price fluctuations into account.

[0691] Step 6:

[0692] The server collects reliable trading partner information and selects suitable buyers. Input is past evaluation and review information, and output is recommended trading partner information. Users can use this information to make informed decisions about which buyer will offer the most favorable terms.

[0693] (Application Example 1)

[0694] 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".

[0695] When individuals sell their personal belongings, accurately determining their market value, finding the optimal selling time, and discovering a trustworthy buyer are all challenging. Furthermore, understanding market trends and demand in real time and conducting transactions under the most favorable conditions is also a challenge. Moreover, there is a lack of readily available means for users to access this information.

[0696] 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.

[0697] In this invention, the server includes means for capturing digital images of items owned by an individual and registering them in an information processing device; means for analyzing the captured digital images using a machine learning algorithm and automatically extracting characteristic information of the items; and means for referring to transaction information in a database and predicting the current market value of the items based on the analyzed characteristic information. This allows individuals to understand the market value of their owned items in real time, easily find the optimal time to sell and a reliable buyer, and conduct transactions under the most favorable conditions.

[0698] "Means of taking digital images of personal belongings and registering them in an information processing device" refers to a method of taking images of personal belongings with a digital camera or similar device, and inputting and saving that image data into a system.

[0699] "A means of analyzing digitally captured images using machine learning algorithms and automatically extracting characteristic information of items" refers to a method that applies machine learning technology to analyze registered images and automatically identify the brand, model number, and other identifiers of an item.

[0700] "A means of predicting the current market value of an item based on analyzed characteristic information by referring to transaction information in a database" refers to a process for predicting the price of an identified item by utilizing past and present market transaction data in a collected database.

[0701] "A means of analyzing temporal market trends and cyclical demand to calculate and provide the optimal time to sell goods" refers to a method of analyzing market data to identify price fluctuations and demand peaks, and then determining the optimal timing for selling based on those insights.

[0702] "Means of selecting and presenting reliable selling institutions to users" refers to the process of identifying trustworthy vendors as suppliers of goods, taking into account evaluation data and reliability indicators, and providing users with a list of such vendors.

[0703] "Means of providing users with evaluation indicators and evaluation information for selling institutions" refers to a system that presents reviews and evaluation scores of companies that users can use as a reference when selecting a buyer.

[0704] "A means of providing an application program that can be executed on a user's mobile device, thereby presenting market value information and buyer information" refers to a method of providing an application that operates on a mobile device such as a smartphone or tablet and displays price information and buyer information.

[0705] The system for realizing this invention primarily uses mobile information terminals such as smartphones and tablets, and a cloud server. Users take digital images of their belongings with their smartphones and send those images to the server through a dedicated application. The terminals are equipped with an interface for taking photos and inputting them into the application.

[0706] On the server, the backend runs using Flask, built with Python, and TensorFlow machine learning algorithms analyze the received image data. This analysis automatically extracts the brand, model, and other feature information of the items. Based on this feature information, the server references market transaction data stored in a database and runs a program to predict the current market value.

[0707] Furthermore, the server analyzes past market trends and seasonal demand data to calculate the most profitable time to sell goods. It also selects reliable selling agencies and provides users with a list of these agencies based on evaluation criteria. Based on this information, users can determine the optimal timing and conditions for their transactions and then execute them.

[0708] As a concrete example, suppose an individual wants to sell an old wristwatch. They take a picture of it and send it to the system. The system identifies the watch's model and brand and displays its market value. At the same time, it recommends that the best time to sell is before the next new model is released. In this way, users can easily sell their watches under better conditions.

[0709] An example of a prompt using a generative AI model is as follows: "Analyze the market value of this item and suggest the optimal time to sell it and a reliable buyer."

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] Users use their mobile devices to take digital images of items they wish to sell and send the images, along with necessary basic information, to the server via a dedicated application. Input includes the product name, model number, and the captured image file. Output is a notification that the transmission to the server is complete.

[0713] Step 2:

[0714] The server begins processing the received image data and item information, analyzing the images using a machine learning algorithm powered by TensorFlow. This automatically extracts the brand name and model number from the input image. The output is item feature information.

[0715] Step 3:

[0716] The server calculates market value by referencing past transaction information in the database based on the generated feature information. The inputs used are feature information and transaction data, and by matching these, the server predicts the current market price. The output is the predicted market value.

[0717] Step 4:

[0718] The server uses market value results and analyzes past market trends and seasonal demand to calculate the optimal selling time. The input for data analysis includes market value and trend data, and the output is the recommended selling time.

[0719] Step 5:

[0720] The server selects reliable selling agencies based on evaluation metrics and creates a list of candidates. This process uses agency evaluation data as input and outputs a list of reliable agencies.

[0721] Step 6:

[0722] The server returns the final analysis results to the user's terminal, including market value, optimal selling time, and a list of reliable buyers. The input includes the output results from the previous stage, and the output is the display of information in the user's application.

[0723] Step 7:

[0724] The user makes a sale decision based on the information provided. The application then displays an interface where the user can select the next action. The input includes the user's selection, and the output is the selection of the next action.

[0725] 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.

[0726] This invention combines a system that appropriately evaluates the market value of personal belongings and recommends the optimal timing and trading partners for their sale with an emotion engine that recognizes and utilizes the user's emotions. This system mainly consists of a user terminal, a server, and an emotion recognition module.

[0727] Users take pictures of items they are considering selling using a smartphone or other device and input the information within the app. During the input process, the app captures the user's facial expressions and voice to recognize emotions. At this time, an emotion engine analyzes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, etc.) in real time. This information is reflected in the operation of the entire system and used to customize recommended information.

[0728] The terminal sends image data and information registered by the user to the server. The server applies a machine learning model and identifies the characteristics of the item through image analysis. Based on the extracted characteristic information, a process is carried out to predict the market value of the item by referring to market transaction data. Furthermore, the server analyzes past market trends to recommend the optimal time to sell. It also takes into account sentiment data recognized by the sentiment engine and presents the user with optimized buyer information and transaction conditions.

[0729] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the user takes a photo of the watch through the app, and the app detects a slight smile on their face during the photo shoot. This emotional state suggests the user has a positive view of the watch's market value and their expectations for selling it. The server incorporates this emotional information and recommends leading buyers while simultaneously emphasizing positive language to encourage immediate transactions.

[0730] This allows for a more personalized trading experience that takes into account the user's emotions, intention to sell, and level of trust. The system aims to improve the efficiency and comfort of the asset buying and selling process by utilizing user emotions.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] When a user takes pictures of items they are considering selling with their smartphone, they register the images through the application and input basic information about the items. During this process, the user's facial expressions and voice data are collected by the device.

[0734] Step 2:

[0735] The device uses an emotion engine to analyze the user's emotional state in real time from their facial expressions and voice data. The analysis results (for example, whether the user is satisfied or anxious) are digitized and sent to the server along with other data.

[0736] Step 3:

[0737] The server analyzes the received image data using a machine learning model to extract the characteristics of the items (brand name, model, condition, etc.). This characteristic information, combined with a market transaction database, serves as a basis for predicting the market value of the items.

[0738] Step 4:

[0739] The server analyzes past market trends and seasonal demand data to recommend the best time to sell items. It then considers user sentiment data and adjusts how the optimal timing is presented (e.g., using more positive content).

[0740] Step 5:

[0741] The server selects and presents reliable buyers to the user based on market data and sentiment information. This includes evaluation information of the buyers, and recommendation language is used that is tailored to the user's emotional state.

[0742] Step 6:

[0743] Based on the information provided by the user, decisions regarding the buyer and timing of the sale are made. The user's selection is registered in the system, and the transaction begins. During this process, if the user's feelings change, this is recognized again and reflected in the data.

[0744] Step 7:

[0745] After a transaction is completed, the server analyzes the user's sentiment data along with the entire transaction history, and uses this data to improve the system and enhance personalization in the future. This data forms the basis for improving the quality of service in subsequent transactions.

[0746] (Example 2)

[0747] 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".

[0748] Traditional asset trading systems made recommendations based solely on market data, without considering user emotions or subjective judgments. This resulted in insufficient consideration of user satisfaction when making trading decisions. Consequently, there was a risk of decreased user confidence in the recommendations provided and reduced satisfaction with trading results.

[0749] 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.

[0750] In this invention, the server includes means for capturing and registering images of items owned by an individual, means for analyzing the user's facial expressions and voice using emotion recognition technology to acquire emotion data, and means for customizing sales information taking the emotion data into consideration. This makes it possible to provide personalized sales recommendations based on the user's emotions.

[0751] "Images of items" refer to digital information that visually captures the items being considered for sale.

[0752] A "machine learning model" is a technology that includes algorithms for analyzing data such as images and identifying features and patterns based on that data.

[0753] "Market value" refers to the expected price or economic value of a particular item when it is traded in the market.

[0754] "Market trends" refer to market movements and general tendencies based on past trading data and supply and demand fluctuations.

[0755] "Emotion recognition technology" is a technology that identifies a person's emotional state from their facial expressions and voice.

[0756] "Emotional data" refers to information that expresses a user's emotions in a numerical or categorical form.

[0757] A "buyer" refers to a trading partner, such as a business or individual, who may potentially purchase the goods.

[0758] "Customization" is the process of optimizing and individualizing information and services based on specific criteria and conditions.

[0759] This invention is a system that appropriately evaluates the market value of personal belongings and recommends the optimal buying and selling timing and trading partners, incorporating technology to recognize and utilize user emotions. The system mainly consists of a user terminal, a server, and an emotion recognition module.

[0760] Users take pictures of items they are considering selling using a smartphone or other device, and input information about the items within the app. The device used here is a personal digital assistant (PDA) equipped with a camera and microphone for image processing and emotion recognition.

[0761] The terminal sends the input image data and item information to the server. The server analyzes the images using a machine learning model and extracts feature information about the items. This machine learning model includes a general-purpose neural network for image recognition. This feature information is used to predict the current market value of the items by referencing a market transaction database.

[0762] Furthermore, the terminal captures the user's facial expressions and voice through an emotion recognition module to obtain the user's emotional data. This emotional data reflects the user's expectations and level of trust regarding the transaction. This emotional data is considered by the server because it influences the selection of proposed buyers and the presentation of sale terms.

[0763] The server further analyzes past market trends and seasonal demand based on the acquired data, providing recommendations for the optimal time to sell items. In this way, users receive personalized selling suggestions based on emotion, allowing them to sell under the most favorable conditions.

[0764] As a concrete example, consider a scenario where a user is trying to sell a luxury watch. Suppose the emotion engine detects that the user is slightly relieved as they take photos of the watch. Based on this emotion data, the server prioritizes recommending favorable and trustworthy buyers and provides the user with information to encourage immediate transaction.

[0765] An example of a prompt to input into a generating AI model is: "Please describe a system that evaluates the market value of an item a user is considering selling, based on images and sentiment data related to the item, and recommends the best buyer and timing."

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The user takes a picture of the item they are considering selling using their device's camera and enters detailed information about the item (brand, model, year of purchase, etc.) into the app. This data is temporarily stored on the device as the item's basic information. This information is then sent to the server in the next step.

[0769] Step 2:

[0770] The device captures the user's facial expressions and voice, and uses a built-in emotion recognition module to analyze the user's emotional data in real time. Inputs include camera video and audio data, and output is the analyzed emotional state (e.g., satisfaction, dissatisfaction, interest, etc.). This emotional data is stored as data indicating the user's attitude towards the transaction.

[0771] Step 3:

[0772] The device transmits collected image data, item information, and emotion data to the server. This data transmission is performed using a secure communication protocol, and the server receives and stores the data.

[0773] Step 4:

[0774] The server receives image data and analyzes the features of the objects using a machine learning model. Specifically, it performs image recognition using a neural network to extract feature information about the objects. The input is image data, and the output is a feature vector that identifies the objects.

[0775] Step 5:

[0776] The server uses the item's characteristic information to refer to a market transaction database and predict the item's market value. Using database queries and statistical models, the system takes the characteristic information as input and generates a predicted market price as output.

[0777] Step 6:

[0778] The server analyzes past market trends and proposes the optimal selling time. This analysis uses time-series data analysis and takes market fluctuation patterns into consideration. As a result, the server proposes a recommended selling time to the user.

[0779] Step 7:

[0780] Based on recognized sentiment data, the server presents the user with buyers and transaction terms optimized for them. This process takes sentiment data as input and outputs buyers and terms likely to be favorable to the user. If positive sentiment is detected, information encouraging immediate transactions is highlighted.

[0781] (Application Example 2)

[0782] 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".

[0783] When individuals sell their belongings, they need optimal market valuation, the right timing for the sale, and reliable buyers. However, providing personalized selling recommendations that take user emotions into consideration is difficult. Furthermore, standard selling processes often fail to adequately reflect user intentions and feelings, resulting in lower satisfaction.

[0784] 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.

[0785] This invention includes a server that includes means for acquiring and registering visual data of items owned by an individual; means for analyzing the acquired visual data using a machine learning model and extracting characteristic information of the items; means for referencing market transaction information and evaluating the current market value of the items based on the analyzed characteristic information; means for analyzing past market trends and demand forecasts and recommending the optimal time to sell the items; and means for evaluating the user's emotional state in real time using an emotion analysis engine and optimizing the user's intention to sell the items. This makes it possible to provide personalized recommendations based on the user's emotions and improve satisfaction with the selling process.

[0786] "Visual data of personal belongings" refers to image and video data acquired to identify personal belongings owned by an individual.

[0787] A "machine learning model" is a computational algorithm that extracts features from data and learns patterns to make future predictions and classifications.

[0788] "Characteristic information" refers to information that indicates the unique characteristics of an item, and it serves as basic data for evaluating its market value.

[0789] "Market transaction information" refers to data including the history of buying and selling goods in the market and price trends, and is used to evaluate the market value of goods.

[0790] "Past market trends" refers to data that shows the history of price fluctuations and demand in the market in the past.

[0791] "Demand forecasting" refers to the results of an analysis aimed at predicting future trends in market demand for goods.

[0792] "Optimal time" refers to the period when it is believed that the greatest profit can be obtained when selling an item.

[0793] An "emotion analysis engine" is a software system that evaluates a user's emotional state in real time by analyzing their facial expressions and voice.

[0794] "User emotional state" refers to information that indicates the psychological state of the user that influences their decision to sell an item.

[0795] "Personalized recommendations" refer to providing information and suggestions that are customized according to the individual user's characteristics and emotions.

[0796] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server works in conjunction with a terminal that has the function of acquiring visual data of items owned by an individual, and extracts characteristic information of the items using a machine learning model based on that visual data. Specifically, the terminal uses a camera device such as a smartphone to take visual data of items that the user is considering selling, and transmits this data to the server.

[0797] The server uses Microsoft Azure's Face API to perform facial recognition and emotion analysis on the received visual data. It also uses Google Cloud's Speech-to-Text API to convert speech input into text, and uses this information to evaluate the user's emotional state. The extracted characteristic information is stored in Amazon Web Services (AWS) DynamoDB and cross-referenced with market transaction data.

[0798] Market transaction information is analyzed using historical market trends and demand forecast data collected by the server and used to calculate the optimal selling time. Based on the user's emotional state revealed by sentiment analysis, the server suggests personalized buyers and timing. For example, if a user shows a strong interest in a luxury watch, the server will notify the user's terminal in real time of the best buyers and recommendations related to that watch.

[0799] As a specific use case, when a user tries to assess the value of household items using their smartphone, the system performs emotion analysis when the camera takes a picture of an item. If positive emotions are detected, it immediately highlights the item as a recommendation to sell. Prompt messages such as "Analyze the facial expressions the user makes while browsing a product page and notify them of special offers related to that product if they show interest" are used as a reference to enrich the user experience.

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The device uses its camera to photograph items the user is considering selling, acquiring visual data. This visual data is sent to the server as input from the user. The output is transferred to the server as image data.

[0803] Step 2:

[0804] The server analyzes the received image data using a machine learning model to extract characteristic information about the items. The input is image data, and the output is characteristic information about the items derived from the images. This process uses a characteristic extraction algorithm.

[0805] Step 3:

[0806] The server evaluates the market value of an item by referencing market transaction information based on its characteristic data. The input is the characteristic data, and the output is the evaluated market value. This process utilizes data matching with historical transaction data and a valuation model.

[0807] Step 4:

[0808] The server analyzes historical market trends and demand forecast data to calculate the optimal selling time. The input is market value information, and the output is a recommendation for the optimal selling time. This analysis uses a trend analysis algorithm.

[0809] Step 5:

[0810] The device uses the user's camera and microphone to acquire facial and audio data, and an emotion analysis engine evaluates the user's emotional state. The input is the user's emotional data, and the output is the analyzed emotional state. Emotion recognition technology is used in this process.

[0811] Step 6:

[0812] The server suggests personalized buyers and selling conditions to the user based on their emotional state and market value information. The input is emotional state and market value information, and the output is individually customized recommendations. This step involves individual optimization by an AI model.

[0813] Step 7:

[0814] The user receives recommendations from the server on their device and selects the appropriate action. This results in a personalized selling experience. The input is the recommendations from the server, and the output is the user's decision.

[0815] 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.

[0816] 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 those described above. 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 shown 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] A means of taking and registering images of personal belongings,

[0839] A means of analyzing captured images using a machine learning model and extracting characteristic information of objects,

[0840] A means for predicting the current market value of an item based on analyzed characteristic information by referring to market transaction data,

[0841] By analyzing past market trends and seasonal demand, we can recommend the best time to sell goods.

[0842] A means of selecting a reliable buyer and presenting it to the user,

[0843] A means of providing users with reviews and evaluation information on the buyer,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which adjusts the analysis parameters of a machine learning model based on the type of article.

[0847] (Claim 3)

[0848] The system according to claim 1, which improves the accuracy of predicting the market value of an item using additional information entered by the user.

[0849] "Example 1"

[0850] (Claim 1)

[0851] A means of acquiring and registering images of items owned by individuals,

[0852] A means for analyzing images acquired using an artificial intelligence model and extracting characteristic information of objects,

[0853] A means for predicting the current market value of an item based on analyzed characteristic information by referring to market transaction information,

[0854] A method for analyzing past market trends and seasonal characteristics to recommend the optimal time to sell goods,

[0855] A means of selecting reliable business partners and presenting them to users,

[0856] A means of providing users with evaluation and review information of business partners,

[0857] A means of selecting a favorable transaction method based on the evaluation information received by the user,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which adjusts the analysis settings of an artificial intelligence model based on the type of article.

[0861] (Claim 3)

[0862] The system according to claim 1, which improves the accuracy of predicting the market value of goods using supplementary information entered by the user.

[0863] "Application Example 1"

[0864] (Claim 1)

[0865] A means of taking digital images of personal belongings and registering them in an information processing device,

[0866] A means for analyzing captured digital images using machine learning algorithms and automatically extracting characteristic information of objects,

[0867] A means for predicting the current market value of an item based on analyzed characteristic information by referring to transaction information in a database,

[0868] A means of analyzing temporal market trends and cyclical demand to calculate and provide the optimal time to sell goods.

[0869] A means of selecting a reliable selling agency and presenting it to users,

[0870] Means for providing users with evaluation indicators and evaluation information for the selling institution,

[0871] We provide an application program that can be executed on the user's mobile device, thereby providing a means to present market value information and buyer information.

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which dynamically adjusts the analysis settings of a machine learning algorithm based on the type of article.

[0875] (Claim 3)

[0876] The system according to claim 1, which improves the accuracy of predicting the market value of goods by utilizing additional information entered by the user.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means of taking and registering images of personal belongings,

[0880] A means of analyzing captured images using a machine learning model and extracting characteristic information of objects,

[0881] A means for predicting the current market value of an item based on analyzed characteristic information by referring to market transaction data,

[0882] By analyzing past market trends and seasonal demand, we can recommend the best time to sell goods.

[0883] A means of selecting a reliable buyer and presenting it to the user,

[0884] A means of providing users with reviews and evaluation information on the buyer,

[0885] A means of acquiring user emotion data using emotion recognition technology that analyzes the user's facial expressions and voice,

[0886] A method for customizing sales information while taking sentiment data into consideration,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which adjusts the analysis parameters of a machine learning model based on the type of article.

[0890] (Claim 3)

[0891] The system according to claim 1, which improves the accuracy of predicting the market value of an item using additional information entered by the user.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A means of acquiring and registering visual data of items owned by an individual,

[0895] A means for analyzing visual data acquired using a machine learning model and extracting characteristic information of an item,

[0896] A means for evaluating the current market value of an item based on analyzed characteristic information by referring to market transaction information,

[0897] By analyzing past market trends and demand forecasts, we can recommend the optimal time to sell your goods.

[0898] A means of selecting a reliable buyer and presenting it to the user,

[0899] A means to optimize the user's intention to sell goods by evaluating their emotional state in real time using an emotion analysis engine,

[0900] A means of providing users with evaluation information on potential buyers and making personalized recommendations based on their emotional state,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, which adjusts the analysis parameters of a machine learning model based on the category of the item.

[0904] (Claim 3)

[0905] The system according to claim 1, which enhances the accuracy of predicting the market value of goods using supplementary information entered by the user. [Explanation of Symbols]

[0906] 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 of taking and registering images of personal belongings, A means of analyzing captured images using a machine learning model and extracting characteristic information of objects, A means for predicting the current market value of an item based on analyzed characteristic information by referring to market transaction data, By analyzing past market trends and seasonal demand, we can recommend the best time to sell goods. A means of selecting a reliable buyer and presenting it to the user, A means of providing users with reviews and evaluation information on the buyer, A system that includes this.

2. The system according to claim 1, which adjusts the analysis parameters of a machine learning model based on the type of article.

3. The system according to claim 1, which improves the accuracy of predicting the market value of goods using additional information entered by the user.

Images