System

Abstract

A system is provided.SOLUTION: This system includes a means for allowing a user to register a desired item, a means for storing the information of the registered item in a database, a means for analyzing a market trend on the basis of the stored item information, and for calculating an optimal selling period and a recommended selling portal, and a means for notifying a user terminal of the analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previously, users had to spend a lot of time and effort researching market trends to find the best time and portal to sell the items they purchased. As a result, users often missed out on how and when to maximize the value of their items, and were more likely to purchase unnecessary items. An efficient method to solve this problem is needed. [Means for solving the problem]

[0005] The present invention provides a system that allows users to register items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended selling portals. Specifically, the system includes a means for users to register desired items, a means for storing registered item information in a database, a means for analyzing market trends based on the stored item information, calculating the optimal time to sell and recommended selling portals, and a means for notifying users of the analysis results via their terminal. This system allows users to maximize the value of their items without spending a lot of time and effort researching market trends. It also allows users to identify and purchase items whose value is increasing, thereby avoiding the purchase of unnecessary items.

[0006] "User" means any person or entity that utilizes the System to register items and receive information on optimal selling times and selling portals.

[0007] "Item" refers to the goods or services that a User purchases and registers in the System.

[0008] "Means for registration" refers to the interface and process by which a user can enter item information and store that information in the system.

[0009] "Database" refers to a data management system for efficiently storing, managing, and searching information on registered items.

[0010] "Means for analyzing market trends" refers to algorithms and processes that use historical sales data, supply and demand trends, and data obtained from online marketplaces to calculate the optimal time and portal for selling an item.

[0011] The "best time to sell" refers to the time that is estimated to be most likely to maximize the market value of the item.

[0012] "Recommended Selling Portal" refers to the online marketplace or platform that has been analyzed as being the most suitable for selling your items.

[0013] "Means of notification" refers to the mechanism by which the system automatically sends the analysis results to the user's device and provides the user with information on the optimal time to sell and recommended selling portals.

[0014] A "user terminal" is a device used to interface with the system, examples of which include smartphones, tablets, computers, etc. [Brief explanation of the drawings]

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

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

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

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

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0036] The present invention relates to a system that allows users to register desired items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals.

[0037] overview

[0038] This system consists of a process for users to register the items they have purchased, a process for saving the item information in a database, a process for conducting analysis based on market trends, and a process for notifying users of the results of the analysis.

[0039] System Components

[0040] 1. User Interface:

[0041] The terminal provides a graphical user interface (GUI) for users to register items.

[0042] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0043] 2. Database:

[0044] The server has a database for storing item information.

[0045] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0046] 3. Market trend analysis algorithm:

[0047] The server collects market data and uses specified algorithms to calculate the best time to sell an item and the recommended selling portal.

[0048] The algorithm uses past months of buying and selling data, supply and demand trends, and real-time data from online marketplaces.

[0049] 4. Notification system:

[0050] The server notifies the user terminal of the analysis results.

[0051] The device will send users push and in-app notifications, displaying information about the best time to sell and recommended selling portals.

[0052] Program processing overview

[0053] 1. User Registration:

[0054] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[0055] 2. Data transmission and storage:

[0056] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[0057] 3. Market Trend Analysis:

[0058] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[0059] 4. Notices and Displays:

[0060] The server then pushes the analysis results to the device, which then receives the notification and recommends selling to the user. When the user taps the notification, detailed information is displayed on a dedicated screen within the app.

[0061] Specific examples

[0062] For example, if a user purchases a new smartphone, the system executes as follows:

[0063] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0064] 2. The device sends this information to the server, which stores the data in a database.

[0065] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[0066] 4. The results are sent to the device, which then notifies the user that "the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen."

[0067] This invention allows users to easily determine the best time to sell their purchased items and list them on the right market at the right time, thereby maximizing the value of the items and avoiding careless purchases.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] User registers an item

[0071] The user opens the application and opens the item registration screen.

[0072] Users enter information such as the item name (e.g., iPhone 12), purchase date, and purchase price.

[0073] The user presses the "Register" button.

[0074] Step 2:

[0075] The device sends data to the server

[0076] The terminal converts the data entered by the user into JSON format.

[0077] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[0078] Step 3:

[0079] The server receives the data and stores it in the database

[0080] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[0081] The server validates the parsed data to ensure all required fields are present.

[0082] The server opens a database connection, creates a record for the new item, and saves it to the database.

[0083] Step 4:

[0084] Start of market trend analysis

[0085] The server periodically analyzes market trends based on the registered item information.

[0086] Analytical algorithms collect data from past sales and online marketplaces to calculate the best time to sell and the recommended selling portal.

[0087] Step 5:

[0088] Saving analysis results

[0089] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[0090] The database is updated with the latest analysis results for each item.

[0091] Step 6:

[0092] Notification of results

[0093] The server sets a trigger to notify the user's device of the latest analysis results.

[0094] If necessary, we will provide push notifications and in-app notifications according to the notification options you have set.

[0095] Step 7:

[0096] The device displays the results

[0097] The device displays the notification received from the server to the user.

[0098] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[0099] Step 8:

[0100] User executes selling instruction

[0101] The user decides whether or not to proceed with the sale based on the notification.

[0102] The terminal will provide a link and additional information to complete the sale.

[0103] In this way, users can easily find the best time and portal to sell their purchased items through the system, maximizing their value.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] Knowing the best time to sell an item is difficult, and many users end up selling at the wrong time. Therefore, there is a need for a system that provides users with the best time to sell and a recommended selling portal to maximize the value of their items. There is also a need for a means to quickly and effectively notify users of the analysis results.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes a means for a user to register desired items, a means for storing information about the registered items in a database, a means for analyzing market trends based on the stored item information and calculating the optimal time to sell and a recommended selling portal, a means for analyzing market trends using a machine learning algorithm, a means for transmitting information to the user using a push notification service, and a means for notifying the user based on the appropriate time to sell and the market trends. This allows the user to know the optimal time to sell and the recommended selling portal to maximize the value of the item. Furthermore, by notifying the user of the analysis results quickly and effectively, the user can sell at the appropriate time.

[0109] "Item" means the object that a User wishes to purchase or sell.

[0110] "User" means any person or organization that uses the System to register items, analyze market trends, or receive notifications.

[0111] "Database" refers to the digital storage system for storing registered item information and for accessing and managing the necessary data.

[0112] "Market Trends" refers to buying and selling tendencies and trends based on past and current market data.

[0113] "Optimal time to sell" refers to the best time to sell an item to maximize its value.

[0114] "Recommended Selling Portal" means the online or offline marketplace best suited to sell an item.

[0115] "Machine learning algorithms" are programs or models used to analyze market trends, making predictions and classifications based on past and current data.

[0116] "Push notification service" refers to a communication method that allows a server to send information to a user device in real time.

[0117] "Means for registration" refers to the method or interface by which a user enters desired items into the system and provides information.

[0118] "Storage means" refers to the process or technology used to store registered item information in a database.

[0119] "Means of notification" refers to the method or system for communicating analysis results and important information to users.

[0120] The present invention relates to a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals. This system is comprised of the following components: a user interface, a database, a market trend analysis algorithm, and a notification system.

[0121] First, the user uses the terminal application to register the purchased item. The user enters detailed information such as the item name, purchase date, and purchase price in the input fields, and then presses the "Register" button to register the item information in the system. This allows the user to easily record and manage item information.

[0122] The registered data is converted to JSON format by the device and sent to the server's API endpoint using a scripting language such as JavaScript, and the data is securely transferred using the SSL / TLS protocol.

[0123] The server first validates the received data to ensure it conforms to a specified format and does not contain any invalid data. This validation is performed using the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server stores the data in a database. This database contains fields such as the item's name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal.

[0124] The server collects market data daily and runs analytical algorithms to analyze market trends. These algorithms are implemented using Python machine learning libraries (e.g., Scikit-learn, TensorFlow) and data analysis libraries (e.g., Pandas, NumPy). Market data includes historical sales data, supply and demand trends, and real-time data from online marketplaces. Based on this data, the server calculates the optimal time to sell registered items and recommends selling portals.

[0125] The server then sends the calculated results to the user's device using a push notification service such as Firebase Cloud Messaging. The notification message contains specific information such as, "The best time to sell this smartphone is on a specific online marketplace in the next three months, with an estimated price of 30,000 yen."

[0126] Finally, users will receive a notification on their device with information about the best time to sell and recommended selling portals. When users tap on the notification, the app will launch and they will be taken to a dedicated screen with more information. This screen will provide detailed information about the best time to sell the registered item and recommended selling portals.

[0127] Examples and prompts

[0128] As a concrete example, consider the case where a user purchases a new smartphone. The user opens the app, enters "new smartphone," and registers the purchase date and purchase price. The device sends this information to the server, which stores the data in a database. The server then analyzes market trends and determines that the best time to sell the smartphone is on a specific online marketplace in a few months' time. This result is notified to the device, and the user receives information such as, "The best time to sell this smartphone is on a specific online marketplace in three months' time, with an expected price of 30,000 yen."

[0129] An example of a prompt to input to a generative AI model is as follows:

[0130] Please explain the specific steps of the system where users use the app to register newly purchased items, analyze market trends, and notify them of the best time to sell and recommended selling portals.

[0131] In this way, by using this system, users can know the best time to sell their purchased items and maximize the value of the items.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] The user registers an item. The user launches the application on their device and accesses the item registration screen. They enter detailed information such as the item name, purchase date, and purchase price into the device's input fields and press the "Register" button.

[0135] Input: Item name, purchase date, purchase price, etc.

[0136] Output: Item information registered on the user's device

[0137] Step 2:

[0138] The device sends data to the server. The device converts the input data into JSON format and then sends it to the server's API endpoint via an HTTP POST request. The data is transferred securely using the SSL / TLS protocol.

[0139] Input: Item information registered on the user's terminal

[0140] Output: Item information sent to the server (JSON format)

[0141] Step 3:

[0142] The server validates the data and saves it to the database. The server first validates the received data to ensure it conforms to the specified format and does not contain any invalid data. This uses the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server saves the data to the database.

[0143] Input: Item information sent to the server (JSON format)

[0144] Output: Item information stored in the database

[0145] Step 4:

[0146] The server analyzes market trends. It periodically collects market data and runs analytical algorithms using Python machine learning libraries (Scikit-learn, TensorFlow) and data analysis libraries (Pandas, NumPy). It uses past sales data for registered items, supply and demand trends, and real-time data from the online market to calculate the optimal time to sell and recommend a sales portal.

[0147] Input: Item information stored in the database, market trend data

[0148] Output: Analysis of the best time to sell and recommended selling portal

[0149] Step 5:

[0150] The server notifies the device of the analysis results. The server uses a push notification service such as Firebase Cloud Messaging to notify the device of the analysis results. The server then configures the notification content and sends it to the user's device.

[0151] Input: Analysis results of optimal time to sell and recommended selling portal

[0152] Output: Notification sent to the user's device (push notification)

[0153] Step 6:

[0154] The device displays the notification. The device displays the push notification received from the server, and when the user taps the notification, the application launches and detailed information is displayed on a dedicated screen.

[0155] Input: Notification received from the server (push notification)

[0156] Output: Detailed information about the best time to sell and recommended selling portals displayed on the user's device

[0157] Examples:

[0158] For example, if a user buys a new smartphone, the system executes as follows:

[0159] 1. The user opens the app, registers a "new smartphone," and enters the purchase date and purchase price.

[0160] 2. The device converts this information into JSON format and sends it to the server.

[0161] 3. The server validates the data and stores it in the database if there are no problems.

[0162] 4. The server analyzes market trends and determines, for example, that a particular online marketplace will be the best place to sell the item in a few months.

[0163] 5. The server pushes the results to the device.

[0164] 6. The device will notify the user that the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen.

[0165] (Application example 1)

[0166] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0167] In conventional systems, there were limited methods for users to determine the optimal time and platform to sell their purchased items, making it difficult to sell efficiently. In addition, users could not immediately understand the analysis results, which sometimes led to missing the timing to sell.

[0168] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0169] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for sending push notifications, and means for displaying the analysis results on a dedicated screen of the terminal. This allows users to receive information on the optimal time to sell and a recommended selling portal in real time, enabling efficient sales.

[0170] The "means for users to register desired items" is an interface that allows users to input information such as the name, purchase date, and purchase price of the purchased item and register it in the system.

[0171] The "means for storing registered item information in a database" is a mechanism for storing and securely maintaining the item information entered by the user in a database.

[0172] The "means for analyzing market trends based on stored item information and calculating the optimal time to sell and the optimal selling portal" refers to a means including an algorithm that utilizes item information in a database, analyzes market data, and calculates the optimal time to sell and the optimal selling portal for the item.

[0173] "Means for notifying the user of the analysis results on the user's device" refers to a mechanism for informing the user of the calculated optimal time to sell and information on recommended selling portals, and includes a notification function.

[0174] "Push notification" refers to a technology that sends messages directly to devices in real time to immediately notify users of important information or the latest analysis results.

[0175] "Means for displaying analysis results on a dedicated screen on the device" refers to means including a screen display function that enables the user to visually check detailed analysis results and recommended information after receiving the notification.

[0176] A "means for recommending the optimal time to purchase an item whose value is likely to increase" is a means that includes an algorithm and a notification function for suggesting the most advantageous time to purchase an item whose value is likely to increase in the future.

[0177] "Past trading data" refers to the record of past trading history and price fluctuations of items, and is the data that forms the basis of analysis.

[0178] "Demand and supply trends" refers to information that indicates changes and trends in market demand and supply for specific items and is used for analysis.

[0179] "Data obtained from online marketplaces" refers to information about sales and purchases collected in real time from online marketplaces and shopping sites.

[0180] The system according to the present invention is implemented through a series of processes to notify users of the best time to sell their purchased items and recommend sales portals. Specific embodiments of the system are described below.

[0181] overview

[0182] The system consists of a user interface, database, market trend analysis algorithm, and notification system. Users register items using a smartphone application, and the data is sent to and stored on a server. The server collects and analyzes market data, notifying users of the best time to sell and recommending a sales portal.

[0183] System Components

[0184] User Interface

[0185] The user interface provides a graphical user interface (GUI) for users to register items. Users open the app and enter details of the purchased item (item name, purchase date, purchase price, etc.). This information is sent from the device to the server.

[0186] Database

[0187] The server has a database where registered item information is stored. This database includes fields such as item name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal. Suitable databases include MySQL and PostgreSQL.

[0188] Market Trend Analysis Algorithm

[0189] The server collects past sales data, supply and demand trends, and real-time data obtained from online marketplaces, and uses a specified algorithm (e.g., Prophet or ARIMA) to calculate the optimal time to sell and the recommended selling portal.

[0190] Notification System

[0191] The analysis results are sent to the user's device via push notification using Firebase Cloud Messaging (FCM). When the user taps the notification, detailed analysis results are displayed on a dedicated screen in the app.

[0192] Specific examples

[0193] For example, if a user purchases a new smartphone, the steps are as follows:

[0194] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0195] 2. The device converts this information into JSON format and sends it to the server's API endpoint, which stores the data in a database.

[0196] 3. The server analyzes market trends daily and calculates, for example, "The best time to sell would be 180 days from now, with an estimated selling price of 27,000 yen."

[0197] 4. The result is sent to the device via Firebase Cloud Messaging. When the user taps the notification, more information is displayed in the app.

[0198] Prompt Sentence Examples

[0199] "Create a system that receives users' purchase registration information, analyzes market trends based on that information, and notifies them of the best time to sell and recommends selling portals. It also makes recommendations on which online marketplaces to sell on. Use Firebase Cloud Messaging as a possible notification mechanism."

[0200] In this way, users receive real-time information on the best time to sell the item and recommended selling portals, allowing them to sell efficiently.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] The user opens the smartphone application and enters details of the purchased item (e.g., item name, purchase date, purchase price), which is then temporarily stored by the device.

[0204] Step 2:

[0205] When the user presses the "Register" button, the terminal converts the input data into JSON format and sends it to the server's API endpoint. The input data includes the item name, purchase date, and purchase price, and the output returns a status indicating that data transmission is complete.

[0206] Step 3:

[0207] The server receives the JSON data sent via the API endpoint and validates the format and content of the data. Once validation is complete, the server saves the data to a MySQL or PostgreSQL database. The input is the JSON data and the output is the success status of saving to the database.

[0208] Step 4:

[0209] The item information stored in the database is periodically sent to the market trend analysis algorithm by a scheduled task on the server. The server collects past sales data, supply and demand trends, and real-time data obtained from online markets. The input is the item information in the database and external market data, and the output is the analysis results of the optimal time to sell and the recommended selling portal.

[0210] Step 5:

[0211] The server uses time series analysis algorithms such as Prophet and ARIMA to analyze market trends and calculate the optimal time to sell an item and the recommended selling portal. The input is historical trading data and real-time market data, and the output is the optimal time to sell and the recommended selling portal. Specific operations include data normalization, interpolation, and analysis.

[0212] Step 6:

[0213] After obtaining the analysis results, the server uses Firebase Cloud Messaging (FCM) to send a push notification to the user device. The input is the analysis results, and the output is a notification message to the user device.

[0214] Step 7:

[0215] When a user receives a push notification, tapping the notification launches the app and opens a dedicated screen displaying the analysis results. The input is the notification message, and the output is the display of detailed analysis results. Specific operations include catching the notification tap event and rendering the dedicated screen.

[0216] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0217] The present invention combines a system in which users register their desired items, analyzes market trends based on that information, and notifies them of the best time to sell and recommended sales portals with an emotion engine that recognizes the user's emotions. This system can provide optimal notification content and timing, as well as purchase recommendations, according to the user's emotional state.

[0218] overview

[0219] The system consists of a process for users to register the items they have purchased, a process for storing item information in a database, a process for conducting analysis based on market trends, a process for notifying users of the results of the analysis, and a process for recognizing users' emotions and adjusting the content of the notification.

[0220] System Components

[0221] 1. User Interface:

[0222] The terminal provides a graphical user interface (GUI) for users to register items.

[0223] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0224] 2. Database:

[0225] The server has a database for storing item information.

[0226] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0227] 3. Market trend analysis algorithm:

[0228] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[0229] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[0230] 4. Emotion Engine:

[0231] The device is equipped with an emotion engine to recognize the user's emotions.

[0232] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[0233] 5. Notification system:

[0234] The server notifies the user terminal of the analysis results.

[0235] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[0236] Program processing overview

[0237] 1. User Registration:

[0238] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[0239] 2. Data transmission and storage:

[0240] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[0241] 3. Market Trend Analysis:

[0242] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[0243] 4. Collecting and analyzing emotional data:

[0244] The device collects the user's voice data, facial expression data, and body movements in real time and analyzes them with an emotion engine to understand the user's emotional state when using apps or receiving notifications.

[0245] 5. Notification of Results:

[0246] The server then pushes the analysis results to the device, which then uses the data from the emotion engine to notify the user at the optimal time and in the optimal format. For example, by sending a notification when the user is in a positive emotional state, the device can achieve effective notifications.

[0247] Specific examples

[0248] For example, if a user purchases a new smartphone, the system executes as follows:

[0249] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0250] 2. The device sends this information to the server, which stores the data in a database.

[0251] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[0252] 4. The device collects the user’s emotional data and sends a notification at the most effective time when the user is in a positive state: “The best time to sell this smartphone on a specific online marketplace in three months is the estimated price of 30,000 yen.”

[0253] 5. The user will receive a notification and can decide whether or not to proceed with the sale based on the content of the notification. A link to proceed with the sale will also be displayed on the notification screen.

[0254] In this way, a system incorporating an emotion engine can provide notifications based on the user's emotional state, providing information at the optimal time and in the optimal way for the user, thereby maximizing the value of items and enabling more effective buying and selling.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] User registers an item

[0258] The user launches the application and opens the item registration screen.

[0259] The user enters information such as the name of the item (e.g., smartphone), purchase date, and purchase price.

[0260] The user presses the "Register" button.

[0261] Step 2:

[0262] The device sends data to the server

[0263] The terminal converts the data entered by the user into JSON format.

[0264] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[0265] Step 3:

[0266] The server receives the data and stores it in the database

[0267] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[0268] The server validates the parsed data to ensure all required fields are present.

[0269] The server opens a database connection, creates a record for the new item, and saves it to the database.

[0270] Step 4:

[0271] Start of market trend analysis

[0272] The server periodically executes a job to analyze market trends based on the registered item information.

[0273] Analytical algorithms use historical sales data, supply and demand trends, and real-time data from online marketplaces to calculate the best time to sell and recommended selling portals.

[0274] Step 5:

[0275] Saving analysis results

[0276] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[0277] The database is updated with the latest analysis results for each item.

[0278] Step 6:

[0279] Collecting user sentiment data

[0280] The device collects the user's voice data, facial expression data, and body movements in real time.

[0281] The collected data is sent to an emotion engine to analyze the emotional state.

[0282] Step 7:

[0283] Adjusting notification content

[0284] The emotion engine determines the optimal timing and content of notifications based on the user's emotional state.

[0285] The emotion engine prioritizes sending notifications when you are in a positive emotional state.

[0286] Step 8:

[0287] Notification of results

[0288] The server pushes the latest analysis results to the user's device.

[0289] Based on data from the emotion engine, the device notifies the user at the optimal time.

[0290] Step 9:

[0291] The device displays the results

[0292] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[0293] The display also provides links to the selling process and additional information.

[0294] Step 10:

[0295] User executes selling instruction

[0296] The user decides whether to proceed with the sale based on the notification.

[0297] The terminal will provide a link and additional information to complete the sale.

[0298] In this way, the present invention is a system that not only notifies users of the optimal time and sales portal for selling purchased items, but also adjusts the content and timing of notifications taking into account the user's emotional state, thereby supporting value maximization for users.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] Conventional systems for recommending the best time to sell items and sales portals send notifications without taking the user's emotional state into consideration, which means that the timing and content of notifications often do not match the user's reactions, making it difficult to provide effective notifications. Furthermore, there was a lack of a mechanism for users to obtain information on the optimal time to sell and purchase recommendations in real time. This made it difficult for users to maximize the value of their items.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes means for users to register desired items, means for saving information about the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for collecting and analyzing user emotion data, and means for adjusting the content and timing of notifications based on the emotion data. This makes it possible to send notifications with appropriate timing and content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of the items.

[0304] "User" means any person or entity that uses the System to register items, receive notifications, and make buying and selling decisions.

[0305] An "item" is an object registered by a user, and refers to an object that can be sold or purchased, such as a commodity, product, or property.

[0306] A "database" is a structured information storage device for storing registered product information, market trend data, analysis results, etc.

[0307] "Market Trends" refers to information that includes past and current sales data, supply and demand trends, and online market activity for a particular item.

[0308] "Selling Portal" means an online marketplace or platform through which users can sell items.

[0309] "Emotional data" is digital data that represents a user's emotional state, collected from their voice, facial expressions, body movements, etc.

[0310] "Notifications" refers to messages or alerts sent to users based on market trend analysis and sentiment data, including information on the best time to sell or recommendations to buy.

[0311] "Collection" is the process of obtaining the necessary data and organizing it appropriately.

[0312] An "analytical algorithm" is a set of calculation procedures or methods for achieving a specific purpose based on collected data, and is used to calculate market trends, the appropriate time to sell, and the sales portal.

[0313] "Push notification" is a communication technology that transmits information to a device in real time, allowing users to receive notifications quickly.

[0314] This system allows users to register their desired items, analyzes market trends based on that information, and notifies them of the optimal time to sell and recommends a sales portal. This system is combined with an emotion engine that recognizes the user's emotions, and can provide optimal notification content and timing according to the user's emotional state.

[0315] System Components

[0316] 1. User Interface:

[0317] The terminal provides a graphical user interface (GUI) for users to register items.

[0318] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0319] 2. Database:

[0320] The server has a database for storing item information.

[0321] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0322] 3. Market trend analysis algorithm:

[0323] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[0324] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[0325] 4. Emotion Engine:

[0326] The device is equipped with an emotion engine to recognize the user's emotions.

[0327] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[0328] 5. Notification system:

[0329] The server notifies the user terminal of the analysis results.

[0330] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[0331] Hardware and software used

[0332] Graphical User Interface (GUI): Used by users when registering items. Uses the GUI library that is standard on the terminal.

[0333] Database: Use a relational database such as MySQL or PostgreSQL on the server side.

[0334] Market trend analysis algorithms: Analytical algorithms implemented in Python or R are used and run on the server via API.

[0335] Emotion engine: Uses Azure Cognitive Services, Google Cloud Vision API, etc. to analyze voice data, facial expression data, and body movements.

[0336] Notification system: Use a push notification service such as Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs).

[0337] Specific examples

[0338] For example, if a user purchases a new smartphone, the system works as follows:

[0339] 1. The user opens the app and registers their new smartphone by entering the name, purchase date, and purchase price.

[0340] 2. The device converts this information into JSON format and sends it to the server.

[0341] 3. The server stores the received data in a database, periodically collects market data, and calculates the optimal time to sell and the recommended selling portal.

[0342] 4. The device collects the user's emotional data (voice, facial expressions, body movements) in real time and optimizes the timing of notifications based on the analysis results.

[0343] 5. The server sends the analysis results, such as "it would be best to sell on a specific online marketplace in three months," to the device and notifies the device at a positive timing based on emotional data.

[0344] Prompt Sentence Examples

[0345] "I've just bought a new smartphone. Can you tell me the best time to sell it and which portal would you recommend?"

[0346] "I'd like to know when I'll sell my next item. The item I use is my smartphone."

[0347] These components and procedures enable notifications to be sent at the appropriate time and with the appropriate content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of items.

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

[0349] System program processing flow

[0350] Step 1: User Registration

[0351] The user opens the application and sees the new item registration screen.

[0352] The user enters information such as the item name, purchase date, and purchase price, and presses the "Register" button.

[0353] Input: Item name, purchase date, purchase price

[0354] Output: Input item information

[0355] Step 2: Data conversion and transmission

[0356] The terminal converts the entered item information into JSON format data.

[0357] The device sends JSON format data as an HTTPS request to the server's API endpoint.

[0358] Input: Item information entered

[0359] Data processing: Convert item information into JSON format

[0360] Output: JSON format data

[0361] Step 3: Receiving and storing data

[0362] The server decodes the received JSON data and validates its format and content.

[0363] The server stores the validated data in a database.

[0364] Input: JSON format data

[0365] Data Operations: Decoding and Verification

[0366] Output: Saved database entries

[0367] Step 4: Gather market data

[0368] The server periodically collects market data through methods such as external APIs and web scraping.

[0369] Input: API requests and data extraction from the web

[0370] Data Computing: Market Data Collection and Organization

[0371] Output: A set of market data

[0372] Step 5: Analyze market trends

[0373] The server uses the collected market data and the item data in the database to run analytical algorithms.

[0374] The server calculates the best time to sell and the recommended selling portal.

[0375] Input: Market data and item data

[0376] Data computation: running analytical algorithms

[0377] Output: Best time to sell and recommended selling portal

[0378] Step 6: Collect emotion data

[0379] The device collects the user's voice data, facial expression data, body movements, etc. in real time.

[0380] Input: User's emotional data (voice, facial expressions, body movements)

[0381] Data collection: Acquiring data from sensors

[0382] Output: Sentiment dataset

[0383] Step 7: Analyze the sentiment data

[0384] The emotional data collected by the device is sent to an emotion engine, which analyzes the user's emotional state in real time.

[0385] Input: Sentiment dataset

[0386] Data Computation: Analysis with an Emotion Engine

[0387] Output: User's emotional state

[0388] Step 8: Generate notifications

[0389] The server generates appropriate notification content based on the market trend analysis results and sentiment data.

[0390] Input: Market trend analysis results, user emotional state

[0391] Data calculation: Notification content generation

[0392] Output: Notification data

[0393] Step 9: Sending notifications

[0394] The notification data generated by the server is sent to the device via a push notification service.

[0395] Input: Notification data

[0396] Data transmission: Use push notification service

[0397] Output: Notification to terminal

[0398] Step 10: Displaying notifications

[0399] The device will notify the user and provide a link to proceed with the sale if necessary.

[0400] Input: Notification data

[0401] Action: Show notification, provide link

[0402] Output: Display a notification to the user

[0403] (Application example 2)

[0404] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] Conventional market trend analysis systems send notifications uniformly without considering the user's emotional state, which can result in insufficient notification effectiveness. Furthermore, there is a need for optimal notification content and timing based on the user's emotional state, in addition to information on the optimal time to sell and recommended sales portals. The present invention aims to solve these problems.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0407] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for providing an emotion engine that recognizes the emotional state of the user, and means for adjusting the content and timing of notifications based on the emotional state and notifying the user terminal of the analysis results, thereby enabling notifications to be sent at the optimal timing and in the optimal manner according to the user's emotional state.

[0408] The "means for users to register desired items" is an interface that allows users to input the products or assets they have purchased or possess into the system and record that information.

[0409] The "means for saving registered item information in a database" is a processing function that saves the item information entered by the user in a database so that it can be accessed and analyzed later.

[0410] "Means for analyzing market trends based on stored item information and calculating the optimal time to sell and recommended selling portal" refers to algorithms and software that use product information stored in a database to analyze market trends and supply and demand trends, and identify the optimal time to sell and sales platform for the user.

[0411] "Means having an emotion engine that recognizes the user's emotional state" refers to an engine or system that analyzes data such as the user's facial expressions, voice, and body movements in real time to grasp the user's emotional state.

[0412] "Means for adjusting notification content and timing based on emotional state and notifying the user of the analysis results on their device" refers to a function that dynamically adjusts notification content and timing based on data obtained from the emotion engine so that the appropriate notification is sent to the user at the optimal time, and sends a push notification to the user's device.

[0413] This invention combines a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the best time to sell and recommends a sales portal, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0414] System configuration

[0415] The system has five main components:

[0416] 1. User Interface (UI):

[0417] It provides a graphical user interface (GUI) for users to register items. This interface is implemented as a smartphone application, and users can enter detailed information about the purchased items (such as name, purchase date, and purchase price).

[0418] 2. Database:

[0419] A database for storing registered item information is placed on the server, including the product name, purchase date, purchase price, estimated selling value, best time to sell, recommended selling portal, etc.

[0420] 3. Market trend analysis algorithm:

[0421] The server collects market data and uses a specific algorithm to calculate the best time to sell an item and the recommended sales portal. The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces. It uses Python's scikit-learn and pandas, and collects data via an API.

[0422] 4. Emotion Engine:

[0423] The smartphone app is equipped with an emotion engine that collects and analyzes the user's voice data, facial expression data, body movements, etc. in real time. The emotion engine can use the Microsoft Azure Emotion API or Google Cloud Vision API.

[0424] 5. Notification system:

[0425] The server then pushes the analysis results to the user's smartphone. Rich notifications are sent to the user's device using Firebase Cloud Messaging (FCM). The server also adjusts the content and timing of notifications based on data from the emotion engine, ensuring optimal notification timing.

[0426] Processing Details

[0427] 1. Data entry and registration:

[0428] The user uses the smartphone app to input the desired item information, including the item name, purchase date, and purchase price. When the user presses the "Register" button, the app sends the input data in JSON format to the server's API endpoint. The server receives the data and stores it in a database.

[0429] 2. Market Trend Analysis:

[0430] The server periodically collects market data and runs analytical algorithms to calculate the best time to sell and recommend a sales portal based on the stored item information. For example, a script written in Python analyzes past data and predicts future market trends.

[0431] 3. Collecting and analyzing emotional data:

[0432] The emotion engine analyzes the user's voice, facial expressions, and body movements in real time to determine their current emotional state, and this analysis data is periodically updated and sent to the server.

[0433] 4. Notification of Results:

[0434] The server integrates market analysis and sentiment engine data to deliver notifications to users at the optimal time and in the optimal way. Notifications are delivered using Firebase Cloud Messaging (FCM), for example, when a user is in a positive emotional state.

[0435] Specific examples

[0436] For example, if a user purchases a new smartphone, the system works as follows:

[0437] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0438] 2. The app sends this information to the server, which stores it in a database.

[0439] 3. The server analyzes market trends daily and determines that it would be best to sell on a specific online marketplace in a few months.

[0440] 4. The app collects the user’s emotional data and sends a notification when the user is in a positive state, saying, “The best time to sell this smartphone on a specific online marketplace in the next three months is the expected price of 30,000 yen.”

[0441] 5. The user will receive a notification and can decide whether to proceed with the sale based on the notification. The notification screen will also display a link to proceed with the sale.

[0442] Prompt Sentence Examples

[0443] I recently bought a new smartphone. I'd like to have an app that lets me register my items, track market trends, and notify me when the best time to sell is. It could also use emotion recognition to notify me when I'm in a positive mood.

[0444] The system thus constructed allows users to maximize the value of the products they have purchased, and by providing notifications at optimal times based on the user's emotional state, it allows them to make more effective sales decisions.

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1:

[0447] Data Entry and Registration

[0448] The user opens the smartphone app and enters the name of the item they purchased, the purchase date, the purchase price, etc. The entered data is converted to JSON format by the device and sent to the server's API endpoint. The server receives this data and stores it in a database. Specifically, the user enters "new smartphone," enters the purchase date and purchase price, and then presses the "Register" button to send the data.

[0449] Input: Item name, purchase date, purchase price

[0450] Output: Item information stored in the database

[0451] Processing: The user enters data, the terminal converts it to JSON format, the server receives the data and saves it to the database

[0452] Step 2:

[0453] Collecting market trend data

[0454] The server periodically collects market data via API, including real-time data from the online market, historical trading data, and supply and demand trends. This data collection is done using scripts written in Python.

[0455] Input: Real-time data obtained from market data API, historical trading data

[0456] Output: Market data stored on the server

[0457] Processing operation: Collect market data through API and store it on the server

[0458] Step 3:

[0459] Market trend analysis

[0460] The server runs an analytical algorithm based on the collected market data. The algorithm uses Python's scikit-learn and pandas to calculate the best time to sell registered items and the recommended sales portal. The algorithm combines historical and real-time data to predict the future value of items.

[0461] Input: Stored market data, product information in the database

[0462] Output: Best time to sell and recommended selling portal

[0463] Processing: Analyzing data with scikit-learn and pandas to predict future market trends

[0464] Step 4:

[0465] Collecting Emotional Data

[0466] The device collects voice data, facial expression data, body movements, etc. in real time to recognize the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Vision API to analyze this data and identify the user's emotional state.

[0467] Input: User's voice data, facial expression data, body movements

[0468] Output: User's emotional state data

[0469] Processing behavior: Emotion engine analyzes data to identify emotional state

[0470] Step 5:

[0471] Sentiment data analysis and integration

[0472] The collected user emotion data is sent to a server and integrated with market trend analysis results. The server then adjusts the content and timing of notifications based on the user's emotional state. For example, if the user is in a positive state and notifications are effective, the server decides to send notifications.

[0473] Input: User emotional state data, market trend analysis results

[0474] Output: Optimal notification content and timing

[0475] Processing behavior: Analyze emotional state data and adjust notification content and timing

[0476] Step 6:

[0477] Notification of results

[0478] The server then sends push notifications to the user's device via Firebase Cloud Messaging (FCM) using the adjusted notification content and timing, including information such as the best time to sell, recommended selling portals, and estimated prices.

[0479] Input: Optimal notification content and timing

[0480] Output: Push notification to user device

[0481] Process behavior: Send a notification using FCM

[0482] Examples:

[0483] For example, if a user buys a new smartphone, they register it in the app by entering "new smartphone," the purchase date, and the purchase price. The server analyzes market trends daily and determines that "it would be best to sell it on a specific online marketplace in a few months." The app collects the user's emotional data and sends a notification when the user is in a positive state. The notification might say, "It would be best to sell this smartphone on a specific online marketplace in three months, with an expected price of 30,000 yen."

[0484] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0491] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0495] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0496] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0497] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0498] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0499] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0500] The present invention relates to a system that allows users to register desired items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals.

[0501] overview

[0502] This system consists of a process for users to register the items they have purchased, a process for saving the item information in a database, a process for conducting analysis based on market trends, and a process for notifying users of the results of the analysis.

[0503] System Components

[0504] 1. User Interface:

[0505] The terminal provides a graphical user interface (GUI) for users to register items.

[0506] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0507] 2. Database:

[0508] The server has a database for storing item information.

[0509] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0510] 3. Market trend analysis algorithm:

[0511] The server collects market data and uses specified algorithms to calculate the best time to sell an item and the recommended selling portal.

[0512] The algorithm uses past months of buying and selling data, supply and demand trends, and real-time data from online marketplaces.

[0513] 4. Notification system:

[0514] The server notifies the user terminal of the analysis results.

[0515] The device will send users push and in-app notifications, displaying information about the best time to sell and recommended selling portals.

[0516] Program processing overview

[0517] 1. User Registration:

[0518] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[0519] 2. Data transmission and storage:

[0520] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[0521] 3. Market Trend Analysis:

[0522] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[0523] 4. Notices and Displays:

[0524] The server then pushes the analysis results to the device, which then receives the notification and recommends selling to the user. When the user taps the notification, detailed information is displayed on a dedicated screen within the app.

[0525] Specific examples

[0526] For example, if a user purchases a new smartphone, the system executes as follows:

[0527] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0528] 2. The device sends this information to the server, which stores the data in a database.

[0529] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[0530] 4. The results are sent to the device, which then notifies the user that "the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen."

[0531] This invention allows users to easily determine the best time to sell their purchased items and list them on the right market at the right time, thereby maximizing the value of the items and avoiding careless purchases.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] User registers an item

[0535] The user opens the application and opens the item registration screen.

[0536] Users enter information such as the item name (e.g., iPhone 12), purchase date, and purchase price.

[0537] The user presses the "Register" button.

[0538] Step 2:

[0539] The device sends data to the server

[0540] The terminal converts the data entered by the user into JSON format.

[0541] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[0542] Step 3:

[0543] The server receives the data and stores it in the database

[0544] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[0545] The server validates the parsed data to ensure all required fields are present.

[0546] The server opens a database connection, creates a record for the new item, and saves it to the database.

[0547] Step 4:

[0548] Start of market trend analysis

[0549] The server periodically analyzes market trends based on the registered item information.

[0550] Analytical algorithms collect data from past sales and online marketplaces to calculate the best time to sell and the recommended selling portal.

[0551] Step 5:

[0552] Saving analysis results

[0553] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[0554] The database is updated with the latest analysis results for each item.

[0555] Step 6:

[0556] Notification of results

[0557] The server sets a trigger to notify the user's device of the latest analysis results.

[0558] If necessary, we will provide push notifications and in-app notifications according to the notification options you have set.

[0559] Step 7:

[0560] The device displays the results

[0561] The device displays the notification received from the server to the user.

[0562] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[0563] Step 8:

[0564] User executes selling instruction

[0565] The user decides whether or not to proceed with the sale based on the notification.

[0566] The terminal will provide a link and additional information to complete the sale.

[0567] In this way, users can easily find the best time and portal to sell their purchased items through the system, maximizing their value.

[0568] Example 1

[0569] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0570] Knowing the best time to sell an item is difficult, and many users end up selling at the wrong time. Therefore, there is a need for a system that provides users with the best time to sell and a recommended selling portal to maximize the value of their items. There is also a need for a means to quickly and effectively notify users of the analysis results.

[0571] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0572] In this invention, the server includes a means for a user to register desired items, a means for storing information about the registered items in a database, a means for analyzing market trends based on the stored item information and calculating the optimal time to sell and a recommended selling portal, a means for analyzing market trends using a machine learning algorithm, a means for transmitting information to the user using a push notification service, and a means for notifying the user based on the appropriate time to sell and the market trends. This allows the user to know the optimal time to sell and the recommended selling portal to maximize the value of the item. Furthermore, by notifying the user of the analysis results quickly and effectively, the user can sell at the appropriate time.

[0573] "Item" means the object that a User wishes to purchase or sell.

[0574] "User" means any person or organization that uses the System to register items, analyze market trends, or receive notifications.

[0575] "Database" refers to the digital storage system for storing registered item information and for accessing and managing the necessary data.

[0576] "Market Trends" refers to buying and selling tendencies and trends based on past and current market data.

[0577] "Optimal time to sell" refers to the best time to sell an item to maximize its value.

[0578] "Recommended Selling Portal" means the online or offline marketplace best suited to sell an item.

[0579] "Machine learning algorithms" are programs or models used to analyze market trends, making predictions and classifications based on past and current data.

[0580] "Push notification service" refers to a communication method that allows a server to send information to a user device in real time.

[0581] "Means for registration" refers to the method or interface by which a user enters desired items into the system and provides information.

[0582] "Storage means" refers to the process or technology used to store registered item information in a database.

[0583] "Means of notification" refers to the method or system for communicating analysis results and important information to users.

[0584] The present invention relates to a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals. This system is comprised of the following components: a user interface, a database, a market trend analysis algorithm, and a notification system.

[0585] First, the user uses the terminal application to register the purchased item. The user enters detailed information such as the item name, purchase date, and purchase price in the input fields, and then presses the "Register" button to register the item information in the system. This allows the user to easily record and manage item information.

[0586] The registered data is converted to JSON format by the device and sent to the server's API endpoint using a scripting language such as JavaScript, and the data is securely transferred using the SSL / TLS protocol.

[0587] The server first validates the received data to ensure it conforms to a specified format and does not contain any invalid data. This validation is performed using the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server stores the data in a database. This database contains fields such as the item's name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal.

[0588] The server collects market data daily and runs analytical algorithms to analyze market trends. These algorithms are implemented using Python machine learning libraries (e.g., Scikit-learn, TensorFlow) and data analysis libraries (e.g., Pandas, NumPy). Market data includes historical sales data, supply and demand trends, and real-time data from online marketplaces. Based on this data, the server calculates the optimal time to sell registered items and recommends selling portals.

[0589] The server then sends the calculated results to the user's device using a push notification service such as Firebase Cloud Messaging. The notification message contains specific information such as, "The best time to sell this smartphone is on a specific online marketplace in the next three months, with an estimated price of 30,000 yen."

[0590] Finally, users will receive a notification on their device with information about the best time to sell and recommended selling portals. When users tap on the notification, the app will launch and they will be taken to a dedicated screen with more information. This screen will provide detailed information about the best time to sell the registered item and recommended selling portals.

[0591] Examples and prompts

[0592] As a concrete example, consider the case where a user purchases a new smartphone. The user opens the app, enters "new smartphone," and registers the purchase date and purchase price. The device sends this information to the server, which stores the data in a database. The server then analyzes market trends and determines that the best time to sell the smartphone is on a specific online marketplace in a few months' time. This result is notified to the device, and the user receives information such as, "The best time to sell this smartphone is on a specific online marketplace in three months' time, with an expected price of 30,000 yen."

[0593] An example of a prompt to input to a generative AI model is as follows:

[0594] Please explain the specific steps of the system where users use the app to register newly purchased items, analyze market trends, and notify them of the best time to sell and recommended selling portals.

[0595] In this way, by using this system, users can know the best time to sell their purchased items and maximize the value of the items.

[0596] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] The user registers an item. The user launches the application on their device and accesses the item registration screen. They enter detailed information such as the item name, purchase date, and purchase price into the device's input fields and press the "Register" button.

[0599] Input: Item name, purchase date, purchase price, etc.

[0600] Output: Item information registered on the user's device

[0601] Step 2:

[0602] The device sends data to the server. The device converts the input data into JSON format and then sends it to the server's API endpoint via an HTTP POST request. The data is transferred securely using the SSL / TLS protocol.

[0603] Input: Item information registered on the user's terminal

[0604] Output: Item information sent to the server (JSON format)

[0605] Step 3:

[0606] The server validates the data and saves it to the database. The server first validates the received data to ensure it conforms to the specified format and does not contain any invalid data. This uses the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server saves the data to the database.

[0607] Input: Item information sent to the server (JSON format)

[0608] Output: Item information stored in the database

[0609] Step 4:

[0610] The server analyzes market trends. It periodically collects market data and runs analytical algorithms using Python machine learning libraries (Scikit-learn, TensorFlow) and data analysis libraries (Pandas, NumPy). It uses past sales data for registered items, supply and demand trends, and real-time data from the online market to calculate the optimal time to sell and recommend a sales portal.

[0611] Input: Item information stored in the database, market trend data

[0612] Output: Analysis of the best time to sell and recommended selling portal

[0613] Step 5:

[0614] The server notifies the device of the analysis results. The server uses a push notification service such as Firebase Cloud Messaging to notify the device of the analysis results. The server then configures the notification content and sends it to the user's device.

[0615] Input: Analysis results of optimal time to sell and recommended selling portal

[0616] Output: Notification sent to the user's device (push notification)

[0617] Step 6:

[0618] The device displays the notification. The device displays the push notification received from the server, and when the user taps the notification, the application launches and detailed information is displayed on a dedicated screen.

[0619] Input: Notification received from the server (push notification)

[0620] Output: Detailed information about the best time to sell and recommended selling portals displayed on the user's device

[0621] Examples:

[0622] For example, if a user buys a new smartphone, the system executes as follows:

[0623] 1. The user opens the app, registers a "new smartphone," and enters the purchase date and purchase price.

[0624] 2. The device converts this information into JSON format and sends it to the server.

[0625] 3. The server validates the data and stores it in the database if there are no problems.

[0626] 4. The server analyzes market trends and determines, for example, that a particular online marketplace will be the best place to sell the item in a few months.

[0627] 5. The server pushes the results to the device.

[0628] 6. The device will notify the user that the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen.

[0629] (Application example 1)

[0630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] In conventional systems, there were limited methods for users to determine the optimal time and platform to sell their purchased items, making it difficult to sell efficiently. In addition, users could not immediately understand the analysis results, which sometimes led to missing the timing to sell.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0633] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for sending push notifications, and means for displaying the analysis results on a dedicated screen of the terminal. This allows users to receive information on the optimal time to sell and a recommended selling portal in real time, enabling efficient sales.

[0634] The "means for users to register desired items" is an interface that allows users to input information such as the name, purchase date, and purchase price of the purchased item and register it in the system.

[0635] The "means for storing registered item information in a database" is a mechanism for storing and securely maintaining the item information entered by the user in a database.

[0636] The "means for analyzing market trends based on stored item information and calculating the optimal time to sell and the optimal selling portal" refers to a means including an algorithm that utilizes item information in a database, analyzes market data, and calculates the optimal time to sell and the optimal selling portal for the item.

[0637] "Means for notifying the user of the analysis results on the user's device" refers to a mechanism for informing the user of the calculated optimal time to sell and information on recommended selling portals, and includes a notification function.

[0638] "Push notification" refers to a technology that sends messages directly to devices in real time to immediately notify users of important information or the latest analysis results.

[0639] "Means for displaying analysis results on a dedicated screen on the device" refers to means including a screen display function that enables the user to visually check detailed analysis results and recommended information after receiving the notification.

[0640] A "means for recommending the optimal time to purchase an item whose value is likely to increase" is a means that includes an algorithm and a notification function for suggesting the most advantageous time to purchase an item whose value is likely to increase in the future.

[0641] "Past trading data" refers to the record of past trading history and price fluctuations of items, and is the data that forms the basis of analysis.

[0642] "Demand and supply trends" refers to information that indicates changes and trends in market demand and supply for specific items and is used for analysis.

[0643] "Data obtained from online marketplaces" refers to information about sales and purchases collected in real time from online marketplaces and shopping sites.

[0644] The system according to the present invention is implemented through a series of processes to notify users of the best time to sell their purchased items and recommend sales portals. Specific embodiments of the system are described below.

[0645] overview

[0646] The system consists of a user interface, database, market trend analysis algorithm, and notification system. Users register items using a smartphone application, and the data is sent to and stored on a server. The server collects and analyzes market data, notifying users of the best time to sell and recommending a sales portal.

[0647] System Components

[0648] User Interface

[0649] The user interface provides a graphical user interface (GUI) for users to register items. Users open the app and enter details of the purchased item (item name, purchase date, purchase price, etc.). This information is sent from the device to the server.

[0650] Database

[0651] The server has a database where registered item information is stored. This database includes fields such as item name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal. Suitable databases include MySQL and PostgreSQL.

[0652] Market Trend Analysis Algorithm

[0653] The server collects past sales data, supply and demand trends, and real-time data obtained from online marketplaces, and uses a specified algorithm (e.g., Prophet or ARIMA) to calculate the optimal time to sell and the recommended selling portal.

[0654] Notification System

[0655] The analysis results are sent to the user's device via push notification using Firebase Cloud Messaging (FCM). When the user taps the notification, detailed analysis results are displayed on a dedicated screen in the app.

[0656] Specific examples

[0657] For example, if a user purchases a new smartphone, the steps are as follows:

[0658] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0659] 2. The device converts this information into JSON format and sends it to the server's API endpoint, which stores the data in a database.

[0660] 3. The server analyzes market trends daily and calculates, for example, "The best time to sell would be 180 days from now, with an estimated selling price of 27,000 yen."

[0661] 4. The result is sent to the device via Firebase Cloud Messaging. When the user taps the notification, more information is displayed in the app.

[0662] Prompt Sentence Examples

[0663] "Create a system that receives users' purchase registration information, analyzes market trends based on that information, and notifies them of the best time to sell and recommends selling portals. It also makes recommendations on which online marketplaces to sell on. Use Firebase Cloud Messaging as a possible notification mechanism."

[0664] In this way, users receive real-time information on the best time to sell the item and recommended selling portals, allowing them to sell efficiently.

[0665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0666] Step 1:

[0667] The user opens the smartphone application and enters details of the purchased item (e.g., item name, purchase date, purchase price), which is then temporarily stored by the device.

[0668] Step 2:

[0669] When the user presses the "Register" button, the terminal converts the input data into JSON format and sends it to the server's API endpoint. The input data includes the item name, purchase date, and purchase price, and the output returns a status indicating that data transmission is complete.

[0670] Step 3:

[0671] The server receives the JSON data sent via the API endpoint and validates the format and content of the data. Once validation is complete, the server saves the data to a MySQL or PostgreSQL database. The input is the JSON data and the output is the success status of saving to the database.

[0672] Step 4:

[0673] The item information stored in the database is periodically sent to the market trend analysis algorithm by a scheduled task on the server. The server collects past sales data, supply and demand trends, and real-time data obtained from online markets. The input is the item information in the database and external market data, and the output is the analysis results of the optimal time to sell and the recommended selling portal.

[0674] Step 5:

[0675] The server uses time series analysis algorithms such as Prophet and ARIMA to analyze market trends and calculate the optimal time to sell an item and the recommended selling portal. The input is historical trading data and real-time market data, and the output is the optimal time to sell and the recommended selling portal. Specific operations include data normalization, interpolation, and analysis.

[0676] Step 6:

[0677] After obtaining the analysis results, the server uses Firebase Cloud Messaging (FCM) to send a push notification to the user device. The input is the analysis results, and the output is a notification message to the user device.

[0678] Step 7:

[0679] When a user receives a push notification, tapping the notification launches the app and opens a dedicated screen displaying the analysis results. The input is the notification message, and the output is the display of detailed analysis results. Specific operations include catching the notification tap event and rendering the dedicated screen.

[0680] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0681] The present invention combines a system in which users register their desired items, analyzes market trends based on that information, and notifies them of the best time to sell and recommended sales portals with an emotion engine that recognizes the user's emotions. This system can provide optimal notification content and timing, as well as purchase recommendations, according to the user's emotional state.

[0682] overview

[0683] The system consists of a process for users to register the items they have purchased, a process for storing item information in a database, a process for conducting analysis based on market trends, a process for notifying users of the results of the analysis, and a process for recognizing users' emotions and adjusting the content of the notification.

[0684] System Components

[0685] 1. User Interface:

[0686] The terminal provides a graphical user interface (GUI) for users to register items.

[0687] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0688] 2. Database:

[0689] The server has a database for storing item information.

[0690] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0691] 3. Market trend analysis algorithm:

[0692] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[0693] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[0694] 4. Emotion Engine:

[0695] The device is equipped with an emotion engine to recognize the user's emotions.

[0696] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[0697] 5. Notification system:

[0698] The server notifies the user terminal of the analysis results.

[0699] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[0700] Program processing overview

[0701] 1. User Registration:

[0702] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[0703] 2. Data transmission and storage:

[0704] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[0705] 3. Market Trend Analysis:

[0706] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[0707] 4. Collecting and analyzing emotional data:

[0708] The device collects the user's voice data, facial expression data, and body movements in real time and analyzes them with an emotion engine to understand the user's emotional state when using apps or receiving notifications.

[0709] 5. Notification of Results:

[0710] The server then pushes the analysis results to the device, which then uses the data from the emotion engine to notify the user at the optimal time and in the optimal format. For example, by sending a notification when the user is in a positive emotional state, the device can achieve effective notifications.

[0711] Specific examples

[0712] For example, if a user purchases a new smartphone, the system executes as follows:

[0713] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0714] 2. The device sends this information to the server, which stores the data in a database.

[0715] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[0716] 4. The device collects the user’s emotional data and sends a notification at the most effective time when the user is in a positive state: “The best time to sell this smartphone on a specific online marketplace in three months is the estimated price of 30,000 yen.”

[0717] 5. The user will receive a notification and can decide whether or not to proceed with the sale based on the content of the notification. A link to proceed with the sale will also be displayed on the notification screen.

[0718] In this way, a system incorporating an emotion engine can provide notifications based on the user's emotional state, providing information at the optimal time and in the optimal way for the user, thereby maximizing the value of items and enabling more effective buying and selling.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] User registers an item

[0722] The user launches the application and opens the item registration screen.

[0723] The user enters information such as the name of the item (e.g., smartphone), purchase date, and purchase price.

[0724] The user presses the "Register" button.

[0725] Step 2:

[0726] The device sends data to the server

[0727] The terminal converts the data entered by the user into JSON format.

[0728] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[0729] Step 3:

[0730] The server receives the data and stores it in the database

[0731] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[0732] The server validates the parsed data to ensure all required fields are present.

[0733] The server opens a database connection, creates a record for the new item, and saves it to the database.

[0734] Step 4:

[0735] Start of market trend analysis

[0736] The server periodically executes a job to analyze market trends based on the registered item information.

[0737] Analytical algorithms use historical sales data, supply and demand trends, and real-time data from online marketplaces to calculate the best time to sell and recommended selling portals.

[0738] Step 5:

[0739] Saving analysis results

[0740] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[0741] The database is updated with the latest analysis results for each item.

[0742] Step 6:

[0743] Collecting user sentiment data

[0744] The device collects the user's voice data, facial expression data, and body movements in real time.

[0745] The collected data is sent to an emotion engine to analyze the emotional state.

[0746] Step 7:

[0747] Adjusting notification content

[0748] The emotion engine determines the optimal timing and content of notifications based on the user's emotional state.

[0749] The emotion engine prioritizes sending notifications when you are in a positive emotional state.

[0750] Step 8:

[0751] Notification of results

[0752] The server pushes the latest analysis results to the user's device.

[0753] Based on data from the emotion engine, the device notifies the user at the optimal time.

[0754] Step 9:

[0755] The device displays the results

[0756] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[0757] The display also provides links to the selling process and additional information.

[0758] Step 10:

[0759] User executes selling instruction

[0760] The user decides whether to proceed with the sale based on the notification.

[0761] The terminal will provide a link and additional information to complete the sale.

[0762] In this way, the present invention is a system that not only notifies users of the optimal time and sales portal for selling purchased items, but also adjusts the content and timing of notifications taking into account the user's emotional state, thereby supporting value maximization for users.

[0763] Example 2

[0764] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0765] Conventional systems for recommending the best time to sell items and sales portals send notifications without taking the user's emotional state into consideration, which means that the timing and content of notifications often do not match the user's reactions, making it difficult to provide effective notifications. Furthermore, there was a lack of a mechanism for users to obtain information on the optimal time to sell and purchase recommendations in real time. This made it difficult for users to maximize the value of their items.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0767] In this invention, the server includes means for users to register desired items, means for saving information about the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for collecting and analyzing user emotion data, and means for adjusting the content and timing of notifications based on the emotion data. This makes it possible to send notifications with appropriate timing and content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of the items.

[0768] "User" means any person or entity that uses the System to register items, receive notifications, and make buying and selling decisions.

[0769] An "item" is an object registered by a user, and refers to an object that can be sold or purchased, such as a commodity, product, or property.

[0770] A "database" is a structured information storage device for storing registered product information, market trend data, analysis results, etc.

[0771] "Market Trends" refers to information that includes past and current sales data, supply and demand trends, and online market activity for a particular item.

[0772] "Selling Portal" means an online marketplace or platform through which users can sell items.

[0773] "Emotional data" is digital data that represents a user's emotional state, collected from their voice, facial expressions, body movements, etc.

[0774] "Notifications" refers to messages or alerts sent to users based on market trend analysis and sentiment data, including information on the best time to sell or recommendations to buy.

[0775] "Collection" is the process of obtaining the necessary data and organizing it appropriately.

[0776] An "analytical algorithm" is a set of calculation procedures or methods for achieving a specific purpose based on collected data, and is used to calculate market trends, the appropriate time to sell, and the sales portal.

[0777] "Push notification" is a communication technology that transmits information to a device in real time, allowing users to receive notifications quickly.

[0778] This system allows users to register their desired items, analyzes market trends based on that information, and notifies them of the optimal time to sell and recommends a sales portal. This system is combined with an emotion engine that recognizes the user's emotions, and can provide optimal notification content and timing according to the user's emotional state.

[0779] System Components

[0780] 1. User Interface:

[0781] The terminal provides a graphical user interface (GUI) for users to register items.

[0782] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0783] 2. Database:

[0784] The server has a database for storing item information.

[0785] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0786] 3. Market trend analysis algorithm:

[0787] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[0788] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[0789] 4. Emotion Engine:

[0790] The device is equipped with an emotion engine to recognize the user's emotions.

[0791] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[0792] 5. Notification system:

[0793] The server notifies the user terminal of the analysis results.

[0794] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[0795] Hardware and software used

[0796] Graphical User Interface (GUI): Used by users when registering items. Uses the GUI library that is standard on the terminal.

[0797] Database: Use a relational database such as MySQL or PostgreSQL on the server side.

[0798] Market trend analysis algorithms: Analytical algorithms implemented in Python or R are used and run on the server via API.

[0799] Emotion engine: Uses Azure Cognitive Services, Google Cloud Vision API, etc. to analyze voice data, facial expression data, and body movements.

[0800] Notification system: Use a push notification service such as Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs).

[0801] Specific examples

[0802] For example, if a user purchases a new smartphone, the system works as follows:

[0803] 1. The user opens the app and registers their new smartphone by entering the name, purchase date, and purchase price.

[0804] 2. The device converts this information into JSON format and sends it to the server.

[0805] 3. The server stores the received data in a database, periodically collects market data, and calculates the optimal time to sell and the recommended selling portal.

[0806] 4. The device collects the user's emotional data (voice, facial expressions, body movements) in real time and optimizes the timing of notifications based on the analysis results.

[0807] 5. The server sends the analysis results, such as "it would be best to sell on a specific online marketplace in three months," to the device and notifies the device at a positive timing based on emotional data.

[0808] Prompt Sentence Examples

[0809] "I've just bought a new smartphone. Can you tell me the best time to sell it and which portal would you recommend?"

[0810] "I'd like to know when I'll sell my next item. The item I use is my smartphone."

[0811] These components and procedures enable notifications to be sent at the appropriate time and with the appropriate content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of items.

[0812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0813] System program processing flow

[0814] Step 1: User Registration

[0815] The user opens the application and sees the new item registration screen.

[0816] The user enters information such as the item name, purchase date, and purchase price, and presses the "Register" button.

[0817] Input: Item name, purchase date, purchase price

[0818] Output: Input item information

[0819] Step 2: Data conversion and transmission

[0820] The terminal converts the entered item information into JSON format data.

[0821] The device sends JSON format data as an HTTPS request to the server's API endpoint.

[0822] Input: Item information entered

[0823] Data processing: Convert item information into JSON format

[0824] Output: JSON format data

[0825] Step 3: Receiving and storing data

[0826] The server decodes the received JSON data and validates its format and content.

[0827] The server stores the validated data in a database.

[0828] Input: JSON format data

[0829] Data Operations: Decoding and Verification

[0830] Output: Saved database entries

[0831] Step 4: Gather market data

[0832] The server periodically collects market data through methods such as external APIs and web scraping.

[0833] Input: API requests and data extraction from the web

[0834] Data Computing: Market Data Collection and Organization

[0835] Output: A set of market data

[0836] Step 5: Analyze market trends

[0837] The server uses the collected market data and the item data in the database to run analytical algorithms.

[0838] The server calculates the best time to sell and the recommended selling portal.

[0839] Input: Market data and item data

[0840] Data computation: running analytical algorithms

[0841] Output: Best time to sell and recommended selling portal

[0842] Step 6: Collect emotion data

[0843] The device collects the user's voice data, facial expression data, body movements, etc. in real time.

[0844] Input: User's emotional data (voice, facial expressions, body movements)

[0845] Data collection: Acquiring data from sensors

[0846] Output: Sentiment dataset

[0847] Step 7: Analyze the sentiment data

[0848] The emotional data collected by the device is sent to an emotion engine, which analyzes the user's emotional state in real time.

[0849] Input: Sentiment dataset

[0850] Data Computation: Analysis with an Emotion Engine

[0851] Output: User's emotional state

[0852] Step 8: Generate notifications

[0853] The server generates appropriate notification content based on the market trend analysis results and sentiment data.

[0854] Input: Market trend analysis results, user emotional state

[0855] Data calculation: Notification content generation

[0856] Output: Notification data

[0857] Step 9: Sending notifications

[0858] The notification data generated by the server is sent to the device via a push notification service.

[0859] Input: Notification data

[0860] Data transmission: Use push notification service

[0861] Output: Notification to terminal

[0862] Step 10: Displaying notifications

[0863] The device will notify the user and provide a link to proceed with the sale if necessary.

[0864] Input: Notification data

[0865] Action: Show notification, provide link

[0866] Output: Display a notification to the user

[0867] (Application example 2)

[0868] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0869] Conventional market trend analysis systems send notifications uniformly without considering the user's emotional state, which can result in insufficient notification effectiveness. Furthermore, there is a need for optimal notification content and timing based on the user's emotional state, in addition to information on the optimal time to sell and recommended sales portals. The present invention aims to solve these problems.

[0870] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0871] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for providing an emotion engine that recognizes the emotional state of the user, and means for adjusting the content and timing of notifications based on the emotional state and notifying the user terminal of the analysis results, thereby enabling notifications to be sent at the optimal timing and in the optimal manner according to the user's emotional state.

[0872] The "means for users to register desired items" is an interface that allows users to input the products or assets they have purchased or possess into the system and record that information.

[0873] The "means for saving registered item information in a database" is a processing function that saves the item information entered by the user in a database so that it can be accessed and analyzed later.

[0874] "Means for analyzing market trends based on stored item information and calculating the optimal time to sell and recommended selling portal" refers to algorithms and software that use product information stored in a database to analyze market trends and supply and demand trends, and identify the optimal time to sell and sales platform for the user.

[0875] "Means having an emotion engine that recognizes the user's emotional state" refers to an engine or system that analyzes data such as the user's facial expressions, voice, and body movements in real time to grasp the user's emotional state.

[0876] "Means for adjusting notification content and timing based on emotional state and notifying the user of the analysis results on their device" refers to a function that dynamically adjusts notification content and timing based on data obtained from the emotion engine so that the appropriate notification is sent to the user at the optimal time, and sends a push notification to the user's device.

[0877] This invention combines a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the best time to sell and recommends a sales portal, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0878] System configuration

[0879] The system has five main components:

[0880] 1. User Interface (UI):

[0881] It provides a graphical user interface (GUI) for users to register items. This interface is implemented as a smartphone application, and users can enter detailed information about the purchased items (such as name, purchase date, and purchase price).

[0882] 2. Database:

[0883] A database for storing registered item information is placed on the server, including the product name, purchase date, purchase price, estimated selling value, best time to sell, recommended selling portal, etc.

[0884] 3. Market trend analysis algorithm:

[0885] The server collects market data and uses a specific algorithm to calculate the best time to sell an item and the recommended sales portal. The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces. It uses Python's scikit-learn and pandas, and collects data via an API.

[0886] 4. Emotion Engine:

[0887] The smartphone app is equipped with an emotion engine that collects and analyzes the user's voice data, facial expression data, body movements, etc. in real time. The emotion engine can use the Microsoft Azure Emotion API or Google Cloud Vision API.

[0888] 5. Notification system:

[0889] The server then pushes the analysis results to the user's smartphone. Rich notifications are sent to the user's device using Firebase Cloud Messaging (FCM). The server also adjusts the content and timing of notifications based on data from the emotion engine, ensuring optimal notification timing.

[0890] Processing Details

[0891] 1. Data entry and registration:

[0892] The user uses the smartphone app to input the desired item information, including the item name, purchase date, and purchase price. When the user presses the "Register" button, the app sends the input data in JSON format to the server's API endpoint. The server receives the data and stores it in a database.

[0893] 2. Market Trend Analysis:

[0894] The server periodically collects market data and runs analytical algorithms to calculate the best time to sell and recommend a sales portal based on the stored item information. For example, a script written in Python analyzes past data and predicts future market trends.

[0895] 3. Collecting and analyzing emotional data:

[0896] The emotion engine analyzes the user's voice, facial expressions, and body movements in real time to determine their current emotional state, and this analysis data is periodically updated and sent to the server.

[0897] 4. Notification of Results:

[0898] The server integrates market analysis and sentiment engine data to deliver notifications to users at the optimal time and in the optimal way. Notifications are delivered using Firebase Cloud Messaging (FCM), for example, when a user is in a positive emotional state.

[0899] Specific examples

[0900] For example, if a user purchases a new smartphone, the system works as follows:

[0901] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0902] 2. The app sends this information to the server, which stores it in a database.

[0903] 3. The server analyzes market trends daily and determines that it would be best to sell on a specific online marketplace in a few months.

[0904] 4. The app collects the user’s emotional data and sends a notification when the user is in a positive state, saying, “The best time to sell this smartphone on a specific online marketplace in the next three months is the expected price of 30,000 yen.”

[0905] 5. The user will receive a notification and can decide whether to proceed with the sale based on the notification. The notification screen will also display a link to proceed with the sale.

[0906] Prompt Sentence Examples

[0907] I recently bought a new smartphone. I'd like to have an app that lets me register my items, track market trends, and notify me when the best time to sell is. It could also use emotion recognition to notify me when I'm in a positive mood.

[0908] The system thus constructed allows users to maximize the value of the products they have purchased, and by providing notifications at optimal times based on the user's emotional state, it allows them to make more effective sales decisions.

[0909] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0910] Step 1:

[0911] Data Entry and Registration

[0912] The user opens the smartphone app and enters the name of the item they purchased, the purchase date, the purchase price, etc. The entered data is converted to JSON format by the device and sent to the server's API endpoint. The server receives this data and stores it in a database. Specifically, the user enters "new smartphone," enters the purchase date and purchase price, and then presses the "Register" button to send the data.

[0913] Input: Item name, purchase date, purchase price

[0914] Output: Item information stored in the database

[0915] Processing: The user enters data, the terminal converts it to JSON format, the server receives the data and saves it to the database

[0916] Step 2:

[0917] Collecting market trend data

[0918] The server periodically collects market data via API, including real-time data from the online market, historical trading data, and supply and demand trends. This data collection is done using scripts written in Python.

[0919] Input: Real-time data obtained from market data API, historical trading data

[0920] Output: Market data stored on the server

[0921] Processing operation: Collect market data through API and store it on the server

[0922] Step 3:

[0923] Market trend analysis

[0924] The server runs an analytical algorithm based on the collected market data. The algorithm uses Python's scikit-learn and pandas to calculate the best time to sell registered items and the recommended sales portal. The algorithm combines historical and real-time data to predict the future value of items.

[0925] Input: Stored market data, product information in the database

[0926] Output: Best time to sell and recommended selling portal

[0927] Processing: Analyzing data with scikit-learn and pandas to predict future market trends

[0928] Step 4:

[0929] Collecting Emotional Data

[0930] The device collects voice data, facial expression data, body movements, etc. in real time to recognize the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Vision API to analyze this data and identify the user's emotional state.

[0931] Input: User's voice data, facial expression data, body movements

[0932] Output: User's emotional state data

[0933] Processing behavior: Emotion engine analyzes data to identify emotional state

[0934] Step 5:

[0935] Sentiment data analysis and integration

[0936] The collected user emotion data is sent to a server and integrated with market trend analysis results. The server then adjusts the content and timing of notifications based on the user's emotional state. For example, if the user is in a positive state and notifications are effective, the server decides to send notifications.

[0937] Input: User emotional state data, market trend analysis results

[0938] Output: Optimal notification content and timing

[0939] Processing behavior: Analyze emotional state data and adjust notification content and timing

[0940] Step 6:

[0941] Notification of results

[0942] The server then sends push notifications to the user's device via Firebase Cloud Messaging (FCM) using the adjusted notification content and timing, including information such as the best time to sell, recommended selling portals, and estimated prices.

[0943] Input: Optimal notification content and timing

[0944] Output: Push notification to user device

[0945] Process behavior: Send a notification using FCM

[0946] Examples:

[0947] For example, if a user buys a new smartphone, they register it in the app by entering "new smartphone," the purchase date, and the purchase price. The server analyzes market trends daily and determines that "it would be best to sell it on a specific online marketplace in a few months." The app collects the user's emotional data and sends a notification when the user is in a positive state. The notification might say, "It would be best to sell this smartphone on a specific online marketplace in three months, with an expected price of 30,000 yen."

[0948] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0949] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0950] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0951] [Third embodiment]

[0952] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0953] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0954] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0955] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0956] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0957] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0958] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0959] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0960] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0961] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0962] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0963] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0964] The present invention relates to a system that allows users to register desired items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals.

[0965] overview

[0966] This system consists of a process for users to register the items they have purchased, a process for saving the item information in a database, a process for conducting analysis based on market trends, and a process for notifying users of the results of the analysis.

[0967] System Components

[0968] 1. User Interface:

[0969] The terminal provides a graphical user interface (GUI) for users to register items.

[0970] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[0971] 2. Database:

[0972] The server has a database for storing item information.

[0973] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[0974] 3. Market trend analysis algorithm:

[0975] The server collects market data and uses specified algorithms to calculate the best time to sell an item and the recommended selling portal.

[0976] The algorithm uses past months of buying and selling data, supply and demand trends, and real-time data from online marketplaces.

[0977] 4. Notification system:

[0978] The server notifies the user terminal of the analysis results.

[0979] The device will send users push and in-app notifications, displaying information about the best time to sell and recommended selling portals.

[0980] Program processing overview

[0981] 1. User Registration:

[0982] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[0983] 2. Data transmission and storage:

[0984] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[0985] 3. Market Trend Analysis:

[0986] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[0987] 4. Notices and Displays:

[0988] The server then pushes the analysis results to the device, which then receives the notification and recommends selling to the user. When the user taps the notification, detailed information is displayed on a dedicated screen within the app.

[0989] Specific examples

[0990] For example, if a user purchases a new smartphone, the system executes as follows:

[0991] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[0992] 2. The device sends this information to the server, which stores the data in a database.

[0993] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[0994] 4. The results are sent to the device, which then notifies the user that "the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen."

[0995] This invention allows users to easily determine the best time to sell their purchased items and list them on the right market at the right time, thereby maximizing the value of the items and avoiding careless purchases.

[0996] The processing flow will be explained below.

[0997] Step 1:

[0998] User registers an item

[0999] The user opens the application and opens the item registration screen.

[1000] Users enter information such as the item name (e.g., iPhone 12), purchase date, and purchase price.

[1001] The user presses the "Register" button.

[1002] Step 2:

[1003] The device sends data to the server

[1004] The terminal converts the data entered by the user into JSON format.

[1005] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[1006] Step 3:

[1007] The server receives the data and stores it in the database

[1008] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[1009] The server validates the parsed data to ensure all required fields are present.

[1010] The server opens a database connection, creates a record for the new item, and saves it to the database.

[1011] Step 4:

[1012] Start of market trend analysis

[1013] The server periodically analyzes market trends based on the registered item information.

[1014] Analytical algorithms collect data from past sales and online marketplaces to calculate the best time to sell and the recommended selling portal.

[1015] Step 5:

[1016] Saving analysis results

[1017] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[1018] The database is updated with the latest analysis results for each item.

[1019] Step 6:

[1020] Notification of results

[1021] The server sets a trigger to notify the user's device of the latest analysis results.

[1022] If necessary, we will provide push notifications and in-app notifications according to the notification options you have set.

[1023] Step 7:

[1024] The device displays the results

[1025] The device displays the notification received from the server to the user.

[1026] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[1027] Step 8:

[1028] User executes selling instruction

[1029] The user decides whether or not to proceed with the sale based on the notification.

[1030] The terminal will provide a link and additional information to complete the sale.

[1031] In this way, users can easily find the best time and portal to sell their purchased items through the system, maximizing their value.

[1032] Example 1

[1033] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1034] Knowing the best time to sell an item is difficult, and many users end up selling at the wrong time. Therefore, there is a need for a system that provides users with the best time to sell and a recommended selling portal to maximize the value of their items. There is also a need for a means to quickly and effectively notify users of the analysis results.

[1035] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1036] In this invention, the server includes a means for a user to register desired items, a means for storing information about the registered items in a database, a means for analyzing market trends based on the stored item information and calculating the optimal time to sell and a recommended selling portal, a means for analyzing market trends using a machine learning algorithm, a means for transmitting information to the user using a push notification service, and a means for notifying the user based on the appropriate time to sell and the market trends. This allows the user to know the optimal time to sell and the recommended selling portal to maximize the value of the item. Furthermore, by notifying the user of the analysis results quickly and effectively, the user can sell at the appropriate time.

[1037] "Item" means the object that a User wishes to purchase or sell.

[1038] "User" means any person or organization that uses the System to register items, analyze market trends, or receive notifications.

[1039] "Database" refers to the digital storage system for storing registered item information and for accessing and managing the necessary data.

[1040] "Market Trends" refers to buying and selling tendencies and trends based on past and current market data.

[1041] "Optimal time to sell" refers to the best time to sell an item to maximize its value.

[1042] "Recommended Selling Portal" means the online or offline marketplace best suited to sell an item.

[1043] "Machine learning algorithms" are programs or models used to analyze market trends, making predictions and classifications based on past and current data.

[1044] "Push notification service" refers to a communication method that allows a server to send information to a user device in real time.

[1045] "Means for registration" refers to the method or interface by which a user enters desired items into the system and provides information.

[1046] "Storage means" refers to the process or technology used to store registered item information in a database.

[1047] "Means of notification" refers to the method or system for communicating analysis results and important information to users.

[1048] The present invention relates to a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals. This system is comprised of the following components: a user interface, a database, a market trend analysis algorithm, and a notification system.

[1049] First, the user uses the terminal application to register the purchased item. The user enters detailed information such as the item name, purchase date, and purchase price in the input fields, and then presses the "Register" button to register the item information in the system. This allows the user to easily record and manage item information.

[1050] The registered data is converted to JSON format by the device and sent to the server's API endpoint using a scripting language such as JavaScript, and the data is securely transferred using the SSL / TLS protocol.

[1051] The server first validates the received data to ensure it conforms to a specified format and does not contain any invalid data. This validation is performed using the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server stores the data in a database. This database contains fields such as the item's name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal.

[1052] The server collects market data daily and runs analytical algorithms to analyze market trends. These algorithms are implemented using Python machine learning libraries (e.g., Scikit-learn, TensorFlow) and data analysis libraries (e.g., Pandas, NumPy). Market data includes historical sales data, supply and demand trends, and real-time data from online marketplaces. Based on this data, the server calculates the optimal time to sell registered items and recommends selling portals.

[1053] The server then sends the calculated results to the user's device using a push notification service such as Firebase Cloud Messaging. The notification message contains specific information such as, "The best time to sell this smartphone is on a specific online marketplace in the next three months, with an estimated price of 30,000 yen."

[1054] Finally, users will receive a notification on their device with information about the best time to sell and recommended selling portals. When users tap on the notification, the app will launch and they will be taken to a dedicated screen with more information. This screen will provide detailed information about the best time to sell the registered item and recommended selling portals.

[1055] Examples and prompts

[1056] As a concrete example, consider the case where a user purchases a new smartphone. The user opens the app, enters "new smartphone," and registers the purchase date and purchase price. The device sends this information to the server, which stores the data in a database. The server then analyzes market trends and determines that the best time to sell the smartphone is on a specific online marketplace in a few months' time. This result is notified to the device, and the user receives information such as, "The best time to sell this smartphone is on a specific online marketplace in three months' time, with an expected price of 30,000 yen."

[1057] An example of a prompt to input to a generative AI model is as follows:

[1058] Please explain the specific steps of the system where users use the app to register newly purchased items, analyze market trends, and notify them of the best time to sell and recommended selling portals.

[1059] In this way, by using this system, users can know the best time to sell their purchased items and maximize the value of the items.

[1060] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1061] Step 1:

[1062] The user registers an item. The user launches the application on their device and accesses the item registration screen. They enter detailed information such as the item name, purchase date, and purchase price into the device's input fields and press the "Register" button.

[1063] Input: Item name, purchase date, purchase price, etc.

[1064] Output: Item information registered on the user's device

[1065] Step 2:

[1066] The device sends data to the server. The device converts the input data into JSON format and then sends it to the server's API endpoint via an HTTP POST request. The data is transferred securely using the SSL / TLS protocol.

[1067] Input: Item information registered on the user's terminal

[1068] Output: Item information sent to the server (JSON format)

[1069] Step 3:

[1070] The server validates the data and saves it to the database. The server first validates the received data to ensure it conforms to the specified format and does not contain any invalid data. This uses the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server saves the data to the database.

[1071] Input: Item information sent to the server (JSON format)

[1072] Output: Item information stored in the database

[1073] Step 4:

[1074] The server analyzes market trends. It periodically collects market data and runs analytical algorithms using Python machine learning libraries (Scikit-learn, TensorFlow) and data analysis libraries (Pandas, NumPy). It uses past sales data for registered items, supply and demand trends, and real-time data from the online market to calculate the optimal time to sell and recommend a sales portal.

[1075] Input: Item information stored in the database, market trend data

[1076] Output: Analysis of the best time to sell and recommended selling portal

[1077] Step 5:

[1078] The server notifies the device of the analysis results. The server uses a push notification service such as Firebase Cloud Messaging to notify the device of the analysis results. The server then configures the notification content and sends it to the user's device.

[1079] Input: Analysis results of optimal time to sell and recommended selling portal

[1080] Output: Notification sent to the user's device (push notification)

[1081] Step 6:

[1082] The device displays the notification. The device displays the push notification received from the server, and when the user taps the notification, the application launches and detailed information is displayed on a dedicated screen.

[1083] Input: Notification received from the server (push notification)

[1084] Output: Detailed information about the best time to sell and recommended selling portals displayed on the user's device

[1085] Examples:

[1086] For example, if a user buys a new smartphone, the system executes as follows:

[1087] 1. The user opens the app, registers a "new smartphone," and enters the purchase date and purchase price.

[1088] 2. The device converts this information into JSON format and sends it to the server.

[1089] 3. The server validates the data and stores it in the database if there are no problems.

[1090] 4. The server analyzes market trends and determines, for example, that a particular online marketplace will be the best place to sell the item in a few months.

[1091] 5. The server pushes the results to the device.

[1092] 6. The device will notify the user that the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen.

[1093] (Application example 1)

[1094] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1095] In conventional systems, there were limited methods for users to determine the optimal time and platform to sell their purchased items, making it difficult to sell efficiently. In addition, users could not immediately understand the analysis results, which sometimes led to missing the timing to sell.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1097] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for sending push notifications, and means for displaying the analysis results on a dedicated screen of the terminal. This allows users to receive information on the optimal time to sell and a recommended selling portal in real time, enabling efficient sales.

[1098] The "means for users to register desired items" is an interface that allows users to input information such as the name, purchase date, and purchase price of the purchased item and register it in the system.

[1099] The "means for storing registered item information in a database" is a mechanism for storing and securely maintaining the item information entered by the user in a database.

[1100] The "means for analyzing market trends based on stored item information and calculating the optimal time to sell and the optimal selling portal" refers to a means including an algorithm that utilizes item information in a database, analyzes market data, and calculates the optimal time to sell and the optimal selling portal for the item.

[1101] "Means for notifying the user of the analysis results on the user's device" refers to a mechanism for informing the user of the calculated optimal time to sell and information on recommended selling portals, and includes a notification function.

[1102] "Push notification" refers to a technology that sends messages directly to devices in real time to immediately notify users of important information or the latest analysis results.

[1103] "Means for displaying analysis results on a dedicated screen on the device" refers to means including a screen display function that enables the user to visually check detailed analysis results and recommended information after receiving the notification.

[1104] A "means for recommending the optimal time to purchase an item whose value is likely to increase" is a means that includes an algorithm and a notification function for suggesting the most advantageous time to purchase an item whose value is likely to increase in the future.

[1105] "Past trading data" refers to the record of past trading history and price fluctuations of items, and is the data that forms the basis of analysis.

[1106] "Demand and supply trends" refers to information that indicates changes and trends in market demand and supply for specific items and is used for analysis.

[1107] "Data obtained from online marketplaces" refers to information about sales and purchases collected in real time from online marketplaces and shopping sites.

[1108] The system according to the present invention is implemented through a series of processes to notify users of the best time to sell their purchased items and recommend sales portals. Specific embodiments of the system are described below.

[1109] overview

[1110] The system consists of a user interface, database, market trend analysis algorithm, and notification system. Users register items using a smartphone application, and the data is sent to and stored on a server. The server collects and analyzes market data, notifying users of the best time to sell and recommending a sales portal.

[1111] System Components

[1112] User Interface

[1113] The user interface provides a graphical user interface (GUI) for users to register items. Users open the app and enter details of the purchased item (item name, purchase date, purchase price, etc.). This information is sent from the device to the server.

[1114] Database

[1115] The server has a database where registered item information is stored. This database includes fields such as item name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal. Suitable databases include MySQL and PostgreSQL.

[1116] Market Trend Analysis Algorithm

[1117] The server collects past sales data, supply and demand trends, and real-time data obtained from online marketplaces, and uses a specified algorithm (e.g., Prophet or ARIMA) to calculate the optimal time to sell and the recommended selling portal.

[1118] Notification System

[1119] The analysis results are sent to the user's device via push notification using Firebase Cloud Messaging (FCM). When the user taps the notification, detailed analysis results are displayed on a dedicated screen in the app.

[1120] Specific examples

[1121] For example, if a user purchases a new smartphone, the steps are as follows:

[1122] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1123] 2. The device converts this information into JSON format and sends it to the server's API endpoint, which stores the data in a database.

[1124] 3. The server analyzes market trends daily and calculates, for example, "The best time to sell would be 180 days from now, with an estimated selling price of 27,000 yen."

[1125] 4. The result is sent to the device via Firebase Cloud Messaging. When the user taps the notification, more information is displayed in the app.

[1126] Prompt Sentence Examples

[1127] "Create a system that receives users' purchase registration information, analyzes market trends based on that information, and notifies them of the best time to sell and recommends selling portals. It also makes recommendations on which online marketplaces to sell on. Use Firebase Cloud Messaging as a possible notification mechanism."

[1128] In this way, users receive real-time information on the best time to sell the item and recommended selling portals, allowing them to sell efficiently.

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] The user opens the smartphone application and enters details of the purchased item (e.g., item name, purchase date, purchase price), which is then temporarily stored by the device.

[1132] Step 2:

[1133] When the user presses the "Register" button, the terminal converts the input data into JSON format and sends it to the server's API endpoint. The input data includes the item name, purchase date, and purchase price, and the output returns a status indicating that data transmission is complete.

[1134] Step 3:

[1135] The server receives the JSON data sent via the API endpoint and validates the format and content of the data. Once validation is complete, the server saves the data to a MySQL or PostgreSQL database. The input is the JSON data and the output is the success status of saving to the database.

[1136] Step 4:

[1137] The item information stored in the database is periodically sent to the market trend analysis algorithm by a scheduled task on the server. The server collects past sales data, supply and demand trends, and real-time data obtained from online markets. The input is the item information in the database and external market data, and the output is the analysis results of the optimal time to sell and the recommended selling portal.

[1138] Step 5:

[1139] The server uses time series analysis algorithms such as Prophet and ARIMA to analyze market trends and calculate the optimal time to sell an item and the recommended selling portal. The input is historical trading data and real-time market data, and the output is the optimal time to sell and the recommended selling portal. Specific operations include data normalization, interpolation, and analysis.

[1140] Step 6:

[1141] After obtaining the analysis results, the server uses Firebase Cloud Messaging (FCM) to send a push notification to the user device. The input is the analysis results, and the output is a notification message to the user device.

[1142] Step 7:

[1143] When a user receives a push notification, tapping the notification launches the app and opens a dedicated screen displaying the analysis results. The input is the notification message, and the output is the display of detailed analysis results. Specific operations include catching the notification tap event and rendering the dedicated screen.

[1144] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1145] The present invention combines a system in which users register their desired items, analyzes market trends based on that information, and notifies them of the best time to sell and recommended sales portals with an emotion engine that recognizes the user's emotions. This system can provide optimal notification content and timing, as well as purchase recommendations, according to the user's emotional state.

[1146] overview

[1147] The system consists of a process for users to register the items they have purchased, a process for storing item information in a database, a process for conducting analysis based on market trends, a process for notifying users of the results of the analysis, and a process for recognizing users' emotions and adjusting the content of the notification.

[1148] System Components

[1149] 1. User Interface:

[1150] The terminal provides a graphical user interface (GUI) for users to register items.

[1151] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[1152] 2. Database:

[1153] The server has a database for storing item information.

[1154] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[1155] 3. Market trend analysis algorithm:

[1156] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[1157] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[1158] 4. Emotion Engine:

[1159] The device is equipped with an emotion engine to recognize the user's emotions.

[1160] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[1161] 5. Notification system:

[1162] The server notifies the user terminal of the analysis results.

[1163] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[1164] Program processing overview

[1165] 1. User Registration:

[1166] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[1167] 2. Data transmission and storage:

[1168] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[1169] 3. Market Trend Analysis:

[1170] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[1171] 4. Collecting and analyzing emotional data:

[1172] The device collects the user's voice data, facial expression data, and body movements in real time and analyzes them with an emotion engine to understand the user's emotional state when using apps or receiving notifications.

[1173] 5. Notification of Results:

[1174] The server then pushes the analysis results to the device, which then uses the data from the emotion engine to notify the user at the optimal time and in the optimal format. For example, by sending a notification when the user is in a positive emotional state, the device can achieve effective notifications.

[1175] Specific examples

[1176] For example, if a user purchases a new smartphone, the system executes as follows:

[1177] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1178] 2. The device sends this information to the server, which stores the data in a database.

[1179] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[1180] 4. The device collects the user’s emotional data and sends a notification at the most effective time when the user is in a positive state: “The best time to sell this smartphone on a specific online marketplace in three months is the estimated price of 30,000 yen.”

[1181] 5. The user will receive a notification and can decide whether or not to proceed with the sale based on the content of the notification. A link to proceed with the sale will also be displayed on the notification screen.

[1182] In this way, a system incorporating an emotion engine can provide notifications based on the user's emotional state, providing information at the optimal time and in the optimal way for the user, thereby maximizing the value of items and enabling more effective buying and selling.

[1183] The processing flow will be explained below.

[1184] Step 1:

[1185] User registers an item

[1186] The user launches the application and opens the item registration screen.

[1187] The user enters information such as the name of the item (e.g., smartphone), purchase date, and purchase price.

[1188] The user presses the "Register" button.

[1189] Step 2:

[1190] The device sends data to the server

[1191] The terminal converts the data entered by the user into JSON format.

[1192] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[1193] Step 3:

[1194] The server receives the data and stores it in the database

[1195] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[1196] The server validates the parsed data to ensure all required fields are present.

[1197] The server opens a database connection, creates a record for the new item, and saves it to the database.

[1198] Step 4:

[1199] Start of market trend analysis

[1200] The server periodically executes a job to analyze market trends based on the registered item information.

[1201] Analytical algorithms use historical sales data, supply and demand trends, and real-time data from online marketplaces to calculate the best time to sell and recommended selling portals.

[1202] Step 5:

[1203] Saving analysis results

[1204] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[1205] The database is updated with the latest analysis results for each item.

[1206] Step 6:

[1207] Collecting user sentiment data

[1208] The device collects the user's voice data, facial expression data, and body movements in real time.

[1209] The collected data is sent to an emotion engine to analyze the emotional state.

[1210] Step 7:

[1211] Adjusting notification content

[1212] The emotion engine determines the optimal timing and content of notifications based on the user's emotional state.

[1213] The emotion engine prioritizes sending notifications when you are in a positive emotional state.

[1214] Step 8:

[1215] Notification of results

[1216] The server pushes the latest analysis results to the user's device.

[1217] Based on data from the emotion engine, the device notifies the user at the optimal time.

[1218] Step 9:

[1219] The device displays the results

[1220] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[1221] The display also provides links to the selling process and additional information.

[1222] Step 10:

[1223] User executes selling instruction

[1224] The user decides whether to proceed with the sale based on the notification.

[1225] The terminal will provide a link and additional information to complete the sale.

[1226] In this way, the present invention is a system that not only notifies users of the optimal time and sales portal for selling purchased items, but also adjusts the content and timing of notifications taking into account the user's emotional state, thereby supporting value maximization for users.

[1227] Example 2

[1228] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1229] Conventional systems for recommending the best time to sell items and sales portals send notifications without taking the user's emotional state into consideration, which means that the timing and content of notifications often do not match the user's reactions, making it difficult to provide effective notifications. Furthermore, there was a lack of a mechanism for users to obtain information on the optimal time to sell and purchase recommendations in real time. This made it difficult for users to maximize the value of their items.

[1230] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1231] In this invention, the server includes means for users to register desired items, means for saving information about the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for collecting and analyzing user emotion data, and means for adjusting the content and timing of notifications based on the emotion data. This makes it possible to send notifications with appropriate timing and content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of the items.

[1232] "User" means any person or entity that uses the System to register items, receive notifications, and make buying and selling decisions.

[1233] An "item" is an object registered by a user, and refers to an object that can be sold or purchased, such as a commodity, product, or property.

[1234] A "database" is a structured information storage device for storing registered product information, market trend data, analysis results, etc.

[1235] "Market Trends" refers to information that includes past and current sales data, supply and demand trends, and online market activity for a particular item.

[1236] "Selling Portal" means an online marketplace or platform through which users can sell items.

[1237] "Emotional data" is digital data that represents a user's emotional state, collected from their voice, facial expressions, body movements, etc.

[1238] "Notifications" refers to messages or alerts sent to users based on market trend analysis and sentiment data, including information on the best time to sell or recommendations to buy.

[1239] "Collection" is the process of obtaining the necessary data and organizing it appropriately.

[1240] An "analytical algorithm" is a set of calculation procedures or methods for achieving a specific purpose based on collected data, and is used to calculate market trends, the appropriate time to sell, and the sales portal.

[1241] "Push notification" is a communication technology that transmits information to a device in real time, allowing users to receive notifications quickly.

[1242] This system allows users to register their desired items, analyzes market trends based on that information, and notifies them of the optimal time to sell and recommends a sales portal. This system is combined with an emotion engine that recognizes the user's emotions, and can provide optimal notification content and timing according to the user's emotional state.

[1243] System Components

[1244] 1. User Interface:

[1245] The terminal provides a graphical user interface (GUI) for users to register items.

[1246] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[1247] 2. Database:

[1248] The server has a database for storing item information.

[1249] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[1250] 3. Market trend analysis algorithm:

[1251] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[1252] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[1253] 4. Emotion Engine:

[1254] The device is equipped with an emotion engine to recognize the user's emotions.

[1255] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[1256] 5. Notification system:

[1257] The server notifies the user terminal of the analysis results.

[1258] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[1259] Hardware and software used

[1260] Graphical User Interface (GUI): Used by users when registering items. Uses the GUI library that is standard on the terminal.

[1261] Database: Use a relational database such as MySQL or PostgreSQL on the server side.

[1262] Market trend analysis algorithms: Analytical algorithms implemented in Python or R are used and run on the server via API.

[1263] Emotion engine: Uses Azure Cognitive Services, Google Cloud Vision API, etc. to analyze voice data, facial expression data, and body movements.

[1264] Notification system: Use a push notification service such as Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs).

[1265] Specific examples

[1266] For example, if a user purchases a new smartphone, the system works as follows:

[1267] 1. The user opens the app and registers their new smartphone by entering the name, purchase date, and purchase price.

[1268] 2. The device converts this information into JSON format and sends it to the server.

[1269] 3. The server stores the received data in a database, periodically collects market data, and calculates the optimal time to sell and the recommended selling portal.

[1270] 4. The device collects the user's emotional data (voice, facial expressions, body movements) in real time and optimizes the timing of notifications based on the analysis results.

[1271] 5. The server sends the analysis results, such as "it would be best to sell on a specific online marketplace in three months," to the device and notifies the device at a positive timing based on emotional data.

[1272] Prompt Sentence Examples

[1273] "I've just bought a new smartphone. Can you tell me the best time to sell it and which portal would you recommend?"

[1274] "I'd like to know when I'll sell my next item. The item I use is my smartphone."

[1275] These components and procedures enable notifications to be sent at the appropriate time and with the appropriate content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of items.

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277] System program processing flow

[1278] Step 1: User Registration

[1279] The user opens the application and sees the new item registration screen.

[1280] The user enters information such as the item name, purchase date, and purchase price, and presses the "Register" button.

[1281] Input: Item name, purchase date, purchase price

[1282] Output: Input item information

[1283] Step 2: Data conversion and transmission

[1284] The terminal converts the entered item information into JSON format data.

[1285] The device sends JSON format data as an HTTPS request to the server's API endpoint.

[1286] Input: Item information entered

[1287] Data processing: Convert item information into JSON format

[1288] Output: JSON format data

[1289] Step 3: Receiving and storing data

[1290] The server decodes the received JSON data and validates its format and content.

[1291] The server stores the validated data in a database.

[1292] Input: JSON format data

[1293] Data Operations: Decoding and Verification

[1294] Output: Saved database entries

[1295] Step 4: Gather market data

[1296] The server periodically collects market data through methods such as external APIs and web scraping.

[1297] Input: API requests and data extraction from the web

[1298] Data Computing: Market Data Collection and Organization

[1299] Output: A set of market data

[1300] Step 5: Analyze market trends

[1301] The server uses the collected market data and the item data in the database to run analytical algorithms.

[1302] The server calculates the best time to sell and the recommended selling portal.

[1303] Input: Market data and item data

[1304] Data computation: running analytical algorithms

[1305] Output: Best time to sell and recommended selling portal

[1306] Step 6: Collect emotion data

[1307] The device collects the user's voice data, facial expression data, body movements, etc. in real time.

[1308] Input: User's emotional data (voice, facial expressions, body movements)

[1309] Data collection: Acquiring data from sensors

[1310] Output: Sentiment dataset

[1311] Step 7: Analyze the sentiment data

[1312] The emotional data collected by the device is sent to an emotion engine, which analyzes the user's emotional state in real time.

[1313] Input: Sentiment dataset

[1314] Data Computation: Analysis with an Emotion Engine

[1315] Output: User's emotional state

[1316] Step 8: Generate notifications

[1317] The server generates appropriate notification content based on the market trend analysis results and sentiment data.

[1318] Input: Market trend analysis results, user emotional state

[1319] Data calculation: Notification content generation

[1320] Output: Notification data

[1321] Step 9: Sending notifications

[1322] The notification data generated by the server is sent to the device via a push notification service.

[1323] Input: Notification data

[1324] Data transmission: Use push notification service

[1325] Output: Notification to terminal

[1326] Step 10: Displaying notifications

[1327] The device will notify the user and provide a link to proceed with the sale if necessary.

[1328] Input: Notification data

[1329] Action: Show notification, provide link

[1330] Output: Display a notification to the user

[1331] (Application example 2)

[1332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Conventional market trend analysis systems send notifications uniformly without considering the user's emotional state, which can result in insufficient notification effectiveness. Furthermore, there is a need for optimal notification content and timing based on the user's emotional state, in addition to information on the optimal time to sell and recommended sales portals. The present invention aims to solve these problems.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1335] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for providing an emotion engine that recognizes the emotional state of the user, and means for adjusting the content and timing of notifications based on the emotional state and notifying the user terminal of the analysis results, thereby enabling notifications to be sent at the optimal timing and in the optimal manner according to the user's emotional state.

[1336] The "means for users to register desired items" is an interface that allows users to input the products or assets they have purchased or possess into the system and record that information.

[1337] The "means for saving registered item information in a database" is a processing function that saves the item information entered by the user in a database so that it can be accessed and analyzed later.

[1338] "Means for analyzing market trends based on stored item information and calculating the optimal time to sell and recommended selling portal" refers to algorithms and software that use product information stored in a database to analyze market trends and supply and demand trends, and identify the optimal time to sell and sales platform for the user.

[1339] "Means having an emotion engine that recognizes the user's emotional state" refers to an engine or system that analyzes data such as the user's facial expressions, voice, and body movements in real time to grasp the user's emotional state.

[1340] "Means for adjusting notification content and timing based on emotional state and notifying the user of the analysis results on their device" refers to a function that dynamically adjusts notification content and timing based on data obtained from the emotion engine so that the appropriate notification is sent to the user at the optimal time, and sends a push notification to the user's device.

[1341] This invention combines a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the best time to sell and recommends a sales portal, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1342] System configuration

[1343] The system has five main components:

[1344] 1. User Interface (UI):

[1345] It provides a graphical user interface (GUI) for users to register items. This interface is implemented as a smartphone application, and users can enter detailed information about the purchased items (such as name, purchase date, and purchase price).

[1346] 2. Database:

[1347] A database for storing registered item information is placed on the server, including the product name, purchase date, purchase price, estimated selling value, best time to sell, recommended selling portal, etc.

[1348] 3. Market trend analysis algorithm:

[1349] The server collects market data and uses a specific algorithm to calculate the best time to sell an item and the recommended sales portal. The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces. It uses Python's scikit-learn and pandas, and collects data via an API.

[1350] 4. Emotion Engine:

[1351] The smartphone app is equipped with an emotion engine that collects and analyzes the user's voice data, facial expression data, body movements, etc. in real time. The emotion engine can use the Microsoft Azure Emotion API or Google Cloud Vision API.

[1352] 5. Notification system:

[1353] The server then pushes the analysis results to the user's smartphone. Rich notifications are sent to the user's device using Firebase Cloud Messaging (FCM). The server also adjusts the content and timing of notifications based on data from the emotion engine, ensuring optimal notification timing.

[1354] Processing Details

[1355] 1. Data entry and registration:

[1356] The user uses the smartphone app to input the desired item information, including the item name, purchase date, and purchase price. When the user presses the "Register" button, the app sends the input data in JSON format to the server's API endpoint. The server receives the data and stores it in a database.

[1357] 2. Market Trend Analysis:

[1358] The server periodically collects market data and runs analytical algorithms to calculate the best time to sell and recommend a sales portal based on the stored item information. For example, a script written in Python analyzes past data and predicts future market trends.

[1359] 3. Collecting and analyzing emotional data:

[1360] The emotion engine analyzes the user's voice, facial expressions, and body movements in real time to determine their current emotional state, and this analysis data is periodically updated and sent to the server.

[1361] 4. Notification of Results:

[1362] The server integrates market analysis and sentiment engine data to deliver notifications to users at the optimal time and in the optimal way. Notifications are delivered using Firebase Cloud Messaging (FCM), for example, when a user is in a positive emotional state.

[1363] Specific examples

[1364] For example, if a user purchases a new smartphone, the system works as follows:

[1365] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1366] 2. The app sends this information to the server, which stores it in a database.

[1367] 3. The server analyzes market trends daily and determines that it would be best to sell on a specific online marketplace in a few months.

[1368] 4. The app collects the user’s emotional data and sends a notification when the user is in a positive state, saying, “The best time to sell this smartphone on a specific online marketplace in the next three months is the expected price of 30,000 yen.”

[1369] 5. The user will receive a notification and can decide whether to proceed with the sale based on the notification. The notification screen will also display a link to proceed with the sale.

[1370] Prompt Sentence Examples

[1371] I recently bought a new smartphone. I'd like to have an app that lets me register my items, track market trends, and notify me when the best time to sell is. It could also use emotion recognition to notify me when I'm in a positive mood.

[1372] The system thus constructed allows users to maximize the value of the products they have purchased, and by providing notifications at optimal times based on the user's emotional state, it allows them to make more effective sales decisions.

[1373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1374] Step 1:

[1375] Data Entry and Registration

[1376] The user opens the smartphone app and enters the name of the item they purchased, the purchase date, the purchase price, etc. The entered data is converted to JSON format by the device and sent to the server's API endpoint. The server receives this data and stores it in a database. Specifically, the user enters "new smartphone," enters the purchase date and purchase price, and then presses the "Register" button to send the data.

[1377] Input: Item name, purchase date, purchase price

[1378] Output: Item information stored in the database

[1379] Processing: The user enters data, the terminal converts it to JSON format, the server receives the data and saves it to the database

[1380] Step 2:

[1381] Collecting market trend data

[1382] The server periodically collects market data via API, including real-time data from the online market, historical trading data, and supply and demand trends. This data collection is done using scripts written in Python.

[1383] Input: Real-time data obtained from market data API, historical trading data

[1384] Output: Market data stored on the server

[1385] Processing operation: Collect market data through API and store it on the server

[1386] Step 3:

[1387] Market trend analysis

[1388] The server runs an analytical algorithm based on the collected market data. The algorithm uses Python's scikit-learn and pandas to calculate the best time to sell registered items and the recommended sales portal. The algorithm combines historical and real-time data to predict the future value of items.

[1389] Input: Stored market data, product information in the database

[1390] Output: Best time to sell and recommended selling portal

[1391] Processing: Analyzing data with scikit-learn and pandas to predict future market trends

[1392] Step 4:

[1393] Collecting Emotional Data

[1394] The device collects voice data, facial expression data, body movements, etc. in real time to recognize the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Vision API to analyze this data and identify the user's emotional state.

[1395] Input: User's voice data, facial expression data, body movements

[1396] Output: User's emotional state data

[1397] Processing behavior: Emotion engine analyzes data to identify emotional state

[1398] Step 5:

[1399] Sentiment data analysis and integration

[1400] The collected user emotion data is sent to a server and integrated with market trend analysis results. The server then adjusts the content and timing of notifications based on the user's emotional state. For example, if the user is in a positive state and notifications are effective, the server decides to send notifications.

[1401] Input: User emotional state data, market trend analysis results

[1402] Output: Optimal notification content and timing

[1403] Processing behavior: Analyze emotional state data and adjust notification content and timing

[1404] Step 6:

[1405] Notification of results

[1406] The server then sends push notifications to the user's device via Firebase Cloud Messaging (FCM) using the adjusted notification content and timing, including information such as the best time to sell, recommended selling portals, and estimated prices.

[1407] Input: Optimal notification content and timing

[1408] Output: Push notification to user device

[1409] Process behavior: Send a notification using FCM

[1410] Examples:

[1411] For example, if a user buys a new smartphone, they register it in the app by entering "new smartphone," the purchase date, and the purchase price. The server analyzes market trends daily and determines that "it would be best to sell it on a specific online marketplace in a few months." The app collects the user's emotional data and sends a notification when the user is in a positive state. The notification might say, "It would be best to sell this smartphone on a specific online marketplace in three months, with an expected price of 30,000 yen."

[1412] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1413] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1414] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1415] [Fourth embodiment]

[1416] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1417] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1418] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1419] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1420] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1422] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1423] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1424] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1425] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1426] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1427] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1428] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] The present invention relates to a system that allows users to register desired items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals.

[1430] overview

[1431] This system consists of a process for users to register the items they have purchased, a process for saving the item information in a database, a process for conducting analysis based on market trends, and a process for notifying users of the results of the analysis.

[1432] System Components

[1433] 1. User Interface:

[1434] The terminal provides a graphical user interface (GUI) for users to register items.

[1435] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[1436] 2. Database:

[1437] The server has a database for storing item information.

[1438] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[1439] 3. Market trend analysis algorithm:

[1440] The server collects market data and uses specified algorithms to calculate the best time to sell an item and the recommended selling portal.

[1441] The algorithm uses past months of buying and selling data, supply and demand trends, and real-time data from online marketplaces.

[1442] 4. Notification system:

[1443] The server notifies the user terminal of the analysis results.

[1444] The device will send users push and in-app notifications, displaying information about the best time to sell and recommended selling portals.

[1445] Program processing overview

[1446] 1. User Registration:

[1447] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[1448] 2. Data transmission and storage:

[1449] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[1450] 3. Market Trend Analysis:

[1451] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[1452] 4. Notices and Displays:

[1453] The server then pushes the analysis results to the device, which then receives the notification and recommends selling to the user. When the user taps the notification, detailed information is displayed on a dedicated screen within the app.

[1454] Specific examples

[1455] For example, if a user purchases a new smartphone, the system executes as follows:

[1456] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1457] 2. The device sends this information to the server, which stores the data in a database.

[1458] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[1459] 4. The results are sent to the device, which then notifies the user that "the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen."

[1460] This invention allows users to easily determine the best time to sell their purchased items and list them on the right market at the right time, thereby maximizing the value of the items and avoiding careless purchases.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] User registers an item

[1464] The user opens the application and opens the item registration screen.

[1465] Users enter information such as the item name (e.g., iPhone 12), purchase date, and purchase price.

[1466] The user presses the "Register" button.

[1467] Step 2:

[1468] The device sends data to the server

[1469] The terminal converts the data entered by the user into JSON format.

[1470] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[1471] Step 3:

[1472] The server receives the data and stores it in the database

[1473] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[1474] The server validates the parsed data to ensure all required fields are present.

[1475] The server opens a database connection, creates a record for the new item, and saves it to the database.

[1476] Step 4:

[1477] Start of market trend analysis

[1478] The server periodically analyzes market trends based on the registered item information.

[1479] Analytical algorithms collect data from past sales and online marketplaces to calculate the best time to sell and the recommended selling portal.

[1480] Step 5:

[1481] Saving analysis results

[1482] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[1483] The database is updated with the latest analysis results for each item.

[1484] Step 6:

[1485] Notification of results

[1486] The server sets a trigger to notify the user's device of the latest analysis results.

[1487] If necessary, we will provide push notifications and in-app notifications according to the notification options you have set.

[1488] Step 7:

[1489] The device displays the results

[1490] The device displays the notification received from the server to the user.

[1491] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[1492] Step 8:

[1493] User executes selling instruction

[1494] The user decides whether or not to proceed with the sale based on the notification.

[1495] The terminal will provide a link and additional information to complete the sale.

[1496] In this way, users can easily find the best time and portal to sell their purchased items through the system, maximizing their value.

[1497] Example 1

[1498] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1499] Knowing the best time to sell an item is difficult, and many users end up selling at the wrong time. Therefore, there is a need for a system that provides users with the best time to sell and a recommended selling portal to maximize the value of their items. There is also a need for a means to quickly and effectively notify users of the analysis results.

[1500] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1501] In this invention, the server includes a means for a user to register desired items, a means for storing information about the registered items in a database, a means for analyzing market trends based on the stored item information and calculating the optimal time to sell and a recommended selling portal, a means for analyzing market trends using a machine learning algorithm, a means for transmitting information to the user using a push notification service, and a means for notifying the user based on the appropriate time to sell and the market trends. This allows the user to know the optimal time to sell and the recommended selling portal to maximize the value of the item. Furthermore, by notifying the user of the analysis results quickly and effectively, the user can sell at the appropriate time.

[1502] "Item" means the object that a User wishes to purchase or sell.

[1503] "User" means any person or organization that uses the System to register items, analyze market trends, or receive notifications.

[1504] "Database" refers to the digital storage system for storing registered item information and for accessing and managing the necessary data.

[1505] "Market Trends" refers to buying and selling tendencies and trends based on past and current market data.

[1506] "Optimal time to sell" refers to the best time to sell an item to maximize its value.

[1507] "Recommended Selling Portal" means the online or offline marketplace best suited to sell an item.

[1508] "Machine learning algorithms" are programs or models used to analyze market trends, making predictions and classifications based on past and current data.

[1509] "Push notification service" refers to a communication method that allows a server to send information to a user device in real time.

[1510] "Means for registration" refers to the method or interface by which a user enters desired items into the system and provides information.

[1511] "Storage means" refers to the process or technology used to store registered item information in a database.

[1512] "Means of notification" refers to the method or system for communicating analysis results and important information to users.

[1513] The present invention relates to a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the optimal time to sell and recommended sales portals. This system is comprised of the following components: a user interface, a database, a market trend analysis algorithm, and a notification system.

[1514] First, the user uses the terminal application to register the purchased item. The user enters detailed information such as the item name, purchase date, and purchase price in the input fields, and then presses the "Register" button to register the item information in the system. This allows the user to easily record and manage item information.

[1515] The registered data is converted to JSON format by the device and sent to the server's API endpoint using a scripting language such as JavaScript, and the data is securely transferred using the SSL / TLS protocol.

[1516] The server first validates the received data to ensure it conforms to a specified format and does not contain any invalid data. This validation is performed using the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server stores the data in a database. This database contains fields such as the item's name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal.

[1517] The server collects market data daily and runs analytical algorithms to analyze market trends. These algorithms are implemented using Python machine learning libraries (e.g., Scikit-learn, TensorFlow) and data analysis libraries (e.g., Pandas, NumPy). Market data includes historical sales data, supply and demand trends, and real-time data from online marketplaces. Based on this data, the server calculates the optimal time to sell registered items and recommends selling portals.

[1518] The server then sends the calculated results to the user's device using a push notification service such as Firebase Cloud Messaging. The notification message contains specific information such as, "The best time to sell this smartphone is on a specific online marketplace in the next three months, with an estimated price of 30,000 yen."

[1519] Finally, users will receive a notification on their device with information about the best time to sell and recommended selling portals. When users tap on the notification, the app will launch and they will be taken to a dedicated screen with more information. This screen will provide detailed information about the best time to sell the registered item and recommended selling portals.

[1520] Examples and prompts

[1521] As a concrete example, consider the case where a user purchases a new smartphone. The user opens the app, enters "new smartphone," and registers the purchase date and purchase price. The device sends this information to the server, which stores the data in a database. The server then analyzes market trends and determines that the best time to sell the smartphone is on a specific online marketplace in a few months' time. This result is notified to the device, and the user receives information such as, "The best time to sell this smartphone is on a specific online marketplace in three months' time, with an expected price of 30,000 yen."

[1522] An example of a prompt to input to a generative AI model is as follows:

[1523] Please explain the specific steps of the system where users use the app to register newly purchased items, analyze market trends, and notify them of the best time to sell and recommended selling portals.

[1524] In this way, by using this system, users can know the best time to sell their purchased items and maximize the value of the items.

[1525] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1526] Step 1:

[1527] The user registers an item. The user launches the application on their device and accesses the item registration screen. They enter detailed information such as the item name, purchase date, and purchase price into the device's input fields and press the "Register" button.

[1528] Input: Item name, purchase date, purchase price, etc.

[1529] Output: Item information registered on the user's device

[1530] Step 2:

[1531] The device sends data to the server. The device converts the input data into JSON format and then sends it to the server's API endpoint via an HTTP POST request. The data is transferred securely using the SSL / TLS protocol.

[1532] Input: Item information registered on the user's terminal

[1533] Output: Item information sent to the server (JSON format)

[1534] Step 3:

[1535] The server validates the data and saves it to the database. The server first validates the received data to ensure it conforms to the specified format and does not contain any invalid data. This uses the validation functions of Python's Flask framework or Django framework. If there are no problems with the validation, the server saves the data to the database.

[1536] Input: Item information sent to the server (JSON format)

[1537] Output: Item information stored in the database

[1538] Step 4:

[1539] The server analyzes market trends. It periodically collects market data and runs analytical algorithms using Python machine learning libraries (Scikit-learn, TensorFlow) and data analysis libraries (Pandas, NumPy). It uses past sales data for registered items, supply and demand trends, and real-time data from the online market to calculate the optimal time to sell and recommend a sales portal.

[1540] Input: Item information stored in the database, market trend data

[1541] Output: Analysis of the best time to sell and recommended selling portal

[1542] Step 5:

[1543] The server notifies the device of the analysis results. The server uses a push notification service such as Firebase Cloud Messaging to notify the device of the analysis results. The server then configures the notification content and sends it to the user's device.

[1544] Input: Analysis results of optimal time to sell and recommended selling portal

[1545] Output: Notification sent to the user's device (push notification)

[1546] Step 6:

[1547] The device displays the notification. The device displays the push notification received from the server, and when the user taps the notification, the application launches and detailed information is displayed on a dedicated screen.

[1548] Input: Notification received from the server (push notification)

[1549] Output: Detailed information about the best time to sell and recommended selling portals displayed on the user's device

[1550] Examples:

[1551] For example, if a user buys a new smartphone, the system executes as follows:

[1552] 1. The user opens the app, registers a "new smartphone," and enters the purchase date and purchase price.

[1553] 2. The device converts this information into JSON format and sends it to the server.

[1554] 3. The server validates the data and stores it in the database if there are no problems.

[1555] 4. The server analyzes market trends and determines, for example, that a particular online marketplace will be the best place to sell the item in a few months.

[1556] 5. The server pushes the results to the device.

[1557] 6. The device will notify the user that the best time to sell this smartphone is on a specific online marketplace in three months, with an estimated price of 30,000 yen.

[1558] (Application example 1)

[1559] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1560] In conventional systems, there were limited methods for users to determine the optimal time and platform to sell their purchased items, making it difficult to sell efficiently. In addition, users could not immediately understand the analysis results, which sometimes led to missing the timing to sell.

[1561] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1562] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for sending push notifications, and means for displaying the analysis results on a dedicated screen of the terminal. This allows users to receive information on the optimal time to sell and a recommended selling portal in real time, enabling efficient sales.

[1563] The "means for users to register desired items" is an interface that allows users to input information such as the name, purchase date, and purchase price of the purchased item and register it in the system.

[1564] The "means for storing registered item information in a database" is a mechanism for storing and securely maintaining the item information entered by the user in a database.

[1565] The "means for analyzing market trends based on stored item information and calculating the optimal time to sell and the optimal selling portal" refers to a means including an algorithm that utilizes item information in a database, analyzes market data, and calculates the optimal time to sell and the optimal selling portal for the item.

[1566] "Means for notifying the user of the analysis results on the user's device" refers to a mechanism for informing the user of the calculated optimal time to sell and information on recommended selling portals, and includes a notification function.

[1567] "Push notification" refers to a technology that sends messages directly to devices in real time to immediately notify users of important information or the latest analysis results.

[1568] "Means for displaying analysis results on a dedicated screen on the device" refers to means including a screen display function that enables the user to visually check detailed analysis results and recommended information after receiving the notification.

[1569] A "means for recommending the optimal time to purchase an item whose value is likely to increase" is a means that includes an algorithm and a notification function for suggesting the most advantageous time to purchase an item whose value is likely to increase in the future.

[1570] "Past trading data" refers to the record of past trading history and price fluctuations of items, and is the data that forms the basis of analysis.

[1571] "Demand and supply trends" refers to information that indicates changes and trends in market demand and supply for specific items and is used for analysis.

[1572] "Data obtained from online marketplaces" refers to information about sales and purchases collected in real time from online marketplaces and shopping sites.

[1573] The system according to the present invention is implemented through a series of processes to notify users of the best time to sell their purchased items and recommend sales portals. Specific embodiments of the system are described below.

[1574] overview

[1575] The system consists of a user interface, database, market trend analysis algorithm, and notification system. Users register items using a smartphone application, and the data is sent to and stored on a server. The server collects and analyzes market data, notifying users of the best time to sell and recommending a sales portal.

[1576] System Components

[1577] User Interface

[1578] The user interface provides a graphical user interface (GUI) for users to register items. Users open the app and enter details of the purchased item (item name, purchase date, purchase price, etc.). This information is sent from the device to the server.

[1579] Database

[1580] The server has a database where registered item information is stored. This database includes fields such as item name, purchase date, purchase price, estimated selling value, best time to sell, and recommended selling portal. Suitable databases include MySQL and PostgreSQL.

[1581] Market Trend Analysis Algorithm

[1582] The server collects past sales data, supply and demand trends, and real-time data obtained from online marketplaces, and uses a specified algorithm (e.g., Prophet or ARIMA) to calculate the optimal time to sell and the recommended selling portal.

[1583] Notification System

[1584] The analysis results are sent to the user's device via push notification using Firebase Cloud Messaging (FCM). When the user taps the notification, detailed analysis results are displayed on a dedicated screen in the app.

[1585] Specific examples

[1586] For example, if a user purchases a new smartphone, the steps are as follows:

[1587] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1588] 2. The device converts this information into JSON format and sends it to the server's API endpoint, which stores the data in a database.

[1589] 3. The server analyzes market trends daily and calculates, for example, "The best time to sell would be 180 days from now, with an estimated selling price of 27,000 yen."

[1590] 4. The result is sent to the device via Firebase Cloud Messaging. When the user taps the notification, more information is displayed in the app.

[1591] Prompt Sentence Examples

[1592] "Create a system that receives users' purchase registration information, analyzes market trends based on that information, and notifies them of the best time to sell and recommends selling portals. It also makes recommendations on which online marketplaces to sell on. Use Firebase Cloud Messaging as a possible notification mechanism."

[1593] In this way, users receive real-time information on the best time to sell the item and recommended selling portals, allowing them to sell efficiently.

[1594] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1595] Step 1:

[1596] The user opens the smartphone application and enters details of the purchased item (e.g., item name, purchase date, purchase price), which is then temporarily stored by the device.

[1597] Step 2:

[1598] When the user presses the "Register" button, the terminal converts the input data into JSON format and sends it to the server's API endpoint. The input data includes the item name, purchase date, and purchase price, and the output returns a status indicating that data transmission is complete.

[1599] Step 3:

[1600] The server receives the JSON data sent via the API endpoint and validates the format and content of the data. Once validation is complete, the server saves the data to a MySQL or PostgreSQL database. The input is the JSON data and the output is the success status of saving to the database.

[1601] Step 4:

[1602] The item information stored in the database is periodically sent to the market trend analysis algorithm by a scheduled task on the server. The server collects past sales data, supply and demand trends, and real-time data obtained from online markets. The input is the item information in the database and external market data, and the output is the analysis results of the optimal time to sell and the recommended selling portal.

[1603] Step 5:

[1604] The server uses time series analysis algorithms such as Prophet and ARIMA to analyze market trends and calculate the optimal time to sell an item and the recommended selling portal. The input is historical trading data and real-time market data, and the output is the optimal time to sell and the recommended selling portal. Specific operations include data normalization, interpolation, and analysis.

[1605] Step 6:

[1606] After obtaining the analysis results, the server uses Firebase Cloud Messaging (FCM) to send a push notification to the user device. The input is the analysis results, and the output is a notification message to the user device.

[1607] Step 7:

[1608] When a user receives a push notification, tapping the notification launches the app and opens a dedicated screen displaying the analysis results. The input is the notification message, and the output is the display of detailed analysis results. Specific operations include catching the notification tap event and rendering the dedicated screen.

[1609] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1610] The present invention combines a system in which users register their desired items, analyzes market trends based on that information, and notifies them of the best time to sell and recommended sales portals with an emotion engine that recognizes the user's emotions. This system can provide optimal notification content and timing, as well as purchase recommendations, according to the user's emotional state.

[1611] overview

[1612] The system consists of a process for users to register the items they have purchased, a process for storing item information in a database, a process for conducting analysis based on market trends, a process for notifying users of the results of the analysis, and a process for recognizing users' emotions and adjusting the content of the notification.

[1613] System Components

[1614] 1. User Interface:

[1615] The terminal provides a graphical user interface (GUI) for users to register items.

[1616] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[1617] 2. Database:

[1618] The server has a database for storing item information.

[1619] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[1620] 3. Market trend analysis algorithm:

[1621] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[1622] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[1623] 4. Emotion Engine:

[1624] The device is equipped with an emotion engine to recognize the user's emotions.

[1625] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[1626] 5. Notification system:

[1627] The server notifies the user terminal of the analysis results.

[1628] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[1629] Program processing overview

[1630] 1. User Registration:

[1631] When a user registers a newly purchased item, they open the application on their device and enter information such as the item name, purchase date, and purchase price. Once the data is entered, they press the "Register" button.

[1632] 2. Data transmission and storage:

[1633] The terminal converts the input data into JSON format and sends it to the server's API endpoint. The server receives the data, validates it, and stores it in the database.

[1634] 3. Market Trend Analysis:

[1635] After the registered data is stored in the database, the server periodically collects market data and runs an analytical algorithm that uses past sales data, supply and demand trends, and real-time data from the online market to calculate the best time to sell and recommend a sales portal.

[1636] 4. Collecting and analyzing emotional data:

[1637] The device collects the user's voice data, facial expression data, and body movements in real time and analyzes them with an emotion engine to understand the user's emotional state when using apps or receiving notifications.

[1638] 5. Notification of Results:

[1639] The server then pushes the analysis results to the device, which then uses the data from the emotion engine to notify the user at the optimal time and in the optimal format. For example, by sending a notification when the user is in a positive emotional state, the device can achieve effective notifications.

[1640] Specific examples

[1641] For example, if a user purchases a new smartphone, the system executes as follows:

[1642] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1643] 2. The device sends this information to the server, which stores the data in a database.

[1644] 3. The server analyzes market trends daily and comes up with results such as: "It will be best to sell on a specific online marketplace in a few months."

[1645] 4. The device collects the user’s emotional data and sends a notification at the most effective time when the user is in a positive state: “The best time to sell this smartphone on a specific online marketplace in three months is the estimated price of 30,000 yen.”

[1646] 5. The user will receive a notification and can decide whether or not to proceed with the sale based on the content of the notification. A link to proceed with the sale will also be displayed on the notification screen.

[1647] In this way, a system incorporating an emotion engine can provide notifications based on the user's emotional state, providing information at the optimal time and in the optimal way for the user, thereby maximizing the value of items and enabling more effective buying and selling.

[1648] The processing flow will be explained below.

[1649] Step 1:

[1650] User registers an item

[1651] The user launches the application and opens the item registration screen.

[1652] The user enters information such as the name of the item (e.g., smartphone), purchase date, and purchase price.

[1653] The user presses the "Register" button.

[1654] Step 2:

[1655] The device sends data to the server

[1656] The terminal converts the data entered by the user into JSON format.

[1657] Send the converted data as an HTTP POST request to the server's API endpoint (e.g., / add-item).

[1658] Step 3:

[1659] The server receives the data and stores it in the database

[1660] The server receives an HTTP request at the / add-item endpoint and parses the request body.

[1661] The server validates the parsed data to ensure all required fields are present.

[1662] The server opens a database connection, creates a record for the new item, and saves it to the database.

[1663] Step 4:

[1664] Start of market trend analysis

[1665] The server periodically executes a job to analyze market trends based on the registered item information.

[1666] Analytical algorithms use historical sales data, supply and demand trends, and real-time data from online marketplaces to calculate the best time to sell and recommended selling portals.

[1667] Step 5:

[1668] Saving analysis results

[1669] The server stores the calculated optimal time to sell and the recommended selling portal in a database.

[1670] The database is updated with the latest analysis results for each item.

[1671] Step 6:

[1672] Collecting user sentiment data

[1673] The device collects the user's voice data, facial expression data, and body movements in real time.

[1674] The collected data is sent to an emotion engine to analyze the emotional state.

[1675] Step 7:

[1676] Adjusting notification content

[1677] The emotion engine determines the optimal timing and content of notifications based on the user's emotional state.

[1678] The emotion engine prioritizes sending notifications when you are in a positive emotional state.

[1679] Step 8:

[1680] Notification of results

[1681] The server pushes the latest analysis results to the user's device.

[1682] Based on data from the emotion engine, the device notifies the user at the optimal time.

[1683] Step 9:

[1684] The device displays the results

[1685] When users tap on the notification, a details screen will open in the app, displaying information on the best time to sell and recommended selling portals.

[1686] The display also provides links to the selling process and additional information.

[1687] Step 10:

[1688] User executes selling instruction

[1689] The user decides whether to proceed with the sale based on the notification.

[1690] The terminal will provide a link and additional information to complete the sale.

[1691] In this way, the present invention is a system that not only notifies users of the optimal time and sales portal for selling purchased items, but also adjusts the content and timing of notifications taking into account the user's emotional state, thereby supporting value maximization for users.

[1692] Example 2

[1693] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1694] Conventional systems for recommending the best time to sell items and sales portals send notifications without taking the user's emotional state into consideration, which means that the timing and content of notifications often do not match the user's reactions, making it difficult to provide effective notifications. Furthermore, there was a lack of a mechanism for users to obtain information on the optimal time to sell and purchase recommendations in real time. This made it difficult for users to maximize the value of their items.

[1695] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1696] In this invention, the server includes means for users to register desired items, means for saving information about the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for notifying the user terminal of the analysis results, means for collecting and analyzing user emotion data, and means for adjusting the content and timing of notifications based on the emotion data. This makes it possible to send notifications with appropriate timing and content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of the items.

[1697] "User" means any person or entity that uses the System to register items, receive notifications, and make buying and selling decisions.

[1698] An "item" is an object registered by a user, and refers to an object that can be sold or purchased, such as a commodity, product, or property.

[1699] A "database" is a structured information storage device for storing registered product information, market trend data, analysis results, etc.

[1700] "Market Trends" refers to information that includes past and current sales data, supply and demand trends, and online market activity for a particular item.

[1701] "Selling Portal" means an online marketplace or platform through which users can sell items.

[1702] "Emotional data" is digital data that represents a user's emotional state, collected from their voice, facial expressions, body movements, etc.

[1703] "Notifications" refers to messages or alerts sent to users based on market trend analysis and sentiment data, including information on the best time to sell or recommendations to buy.

[1704] "Collection" is the process of obtaining the necessary data and organizing it appropriately.

[1705] An "analytical algorithm" is a set of calculation procedures or methods for achieving a specific purpose based on collected data, and is used to calculate market trends, the appropriate time to sell, and the sales portal.

[1706] "Push notification" is a communication technology that transmits information to a device in real time, allowing users to receive notifications quickly.

[1707] This system allows users to register their desired items, analyzes market trends based on that information, and notifies them of the optimal time to sell and recommends a sales portal. This system is combined with an emotion engine that recognizes the user's emotions, and can provide optimal notification content and timing according to the user's emotional state.

[1708] System Components

[1709] 1. User Interface:

[1710] The terminal provides a graphical user interface (GUI) for users to register items.

[1711] The user enters details of the item purchased (such as name, purchase date, and purchase price).

[1712] 2. Database:

[1713] The server has a database for storing item information.

[1714] The database includes fields such as the item's name, purchase date, purchase price, estimated resale value, best time to sell, and recommended selling portal.

[1715] 3. Market trend analysis algorithm:

[1716] The server collects market data and uses a specified algorithm to calculate the best time to sell an item and the recommended selling portal.

[1717] The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces.

[1718] 4. Emotion Engine:

[1719] The device is equipped with an emotion engine to recognize the user's emotions.

[1720] The emotion engine analyzes emotion data such as voice data, facial expression data, and body movements to understand the user's emotional state.

[1721] 5. Notification system:

[1722] The server notifies the user terminal of the analysis results.

[1723] The device uses data from the emotion engine to adjust the content and timing of notifications based on the user's emotional state.

[1724] Hardware and software used

[1725] Graphical User Interface (GUI): Used by users when registering items. Uses the GUI library that is standard on the terminal.

[1726] Database: Use a relational database such as MySQL or PostgreSQL on the server side.

[1727] Market trend analysis algorithms: Analytical algorithms implemented in Python or R are used and run on the server via API.

[1728] Emotion engine: Uses Azure Cognitive Services, Google Cloud Vision API, etc. to analyze voice data, facial expression data, and body movements.

[1729] Notification system: Use a push notification service such as Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs).

[1730] Specific examples

[1731] For example, if a user purchases a new smartphone, the system works as follows:

[1732] 1. The user opens the app and registers their new smartphone by entering the name, purchase date, and purchase price.

[1733] 2. The device converts this information into JSON format and sends it to the server.

[1734] 3. The server stores the received data in a database, periodically collects market data, and calculates the optimal time to sell and the recommended selling portal.

[1735] 4. The device collects the user's emotional data (voice, facial expressions, body movements) in real time and optimizes the timing of notifications based on the analysis results.

[1736] 5. The server sends the analysis results, such as "it would be best to sell on a specific online marketplace in three months," to the device and notifies the device at a positive timing based on emotional data.

[1737] Prompt Sentence Examples

[1738] "I've just bought a new smartphone. Can you tell me the best time to sell it and which portal would you recommend?"

[1739] "I'd like to know when I'll sell my next item. The item I use is my smartphone."

[1740] These components and procedures enable notifications to be sent at the appropriate time and with the appropriate content according to the user's emotional state, allowing the user to effectively make optimal sales or purchases to maximize the value of items.

[1741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1742] System program processing flow

[1743] Step 1: User Registration

[1744] The user opens the application and sees the new item registration screen.

[1745] The user enters information such as the item name, purchase date, and purchase price, and presses the "Register" button.

[1746] Input: Item name, purchase date, purchase price

[1747] Output: Input item information

[1748] Step 2: Data conversion and transmission

[1749] The terminal converts the entered item information into JSON format data.

[1750] The device sends JSON format data as an HTTPS request to the server's API endpoint.

[1751] Input: Item information entered

[1752] Data processing: Convert item information into JSON format

[1753] Output: JSON format data

[1754] Step 3: Receiving and storing data

[1755] The server decodes the received JSON data and validates its format and content.

[1756] The server stores the validated data in a database.

[1757] Input: JSON format data

[1758] Data Operations: Decoding and Verification

[1759] Output: Saved database entries

[1760] Step 4: Gather market data

[1761] The server periodically collects market data through methods such as external APIs and web scraping.

[1762] Input: API requests and data extraction from the web

[1763] Data Computing: Market Data Collection and Organization

[1764] Output: A set of market data

[1765] Step 5: Analyze market trends

[1766] The server uses the collected market data and the item data in the database to run analytical algorithms.

[1767] The server calculates the best time to sell and the recommended selling portal.

[1768] Input: Market data and item data

[1769] Data computation: running analytical algorithms

[1770] Output: Best time to sell and recommended selling portal

[1771] Step 6: Collect emotion data

[1772] The device collects the user's voice data, facial expression data, body movements, etc. in real time.

[1773] Input: User's emotional data (voice, facial expressions, body movements)

[1774] Data collection: Acquiring data from sensors

[1775] Output: Sentiment dataset

[1776] Step 7: Analyze the sentiment data

[1777] The emotional data collected by the device is sent to an emotion engine, which analyzes the user's emotional state in real time.

[1778] Input: Sentiment dataset

[1779] Data Computation: Analysis with an Emotion Engine

[1780] Output: User's emotional state

[1781] Step 8: Generate notifications

[1782] The server generates appropriate notification content based on the market trend analysis results and sentiment data.

[1783] Input: Market trend analysis results, user emotional state

[1784] Data calculation: Notification content generation

[1785] Output: Notification data

[1786] Step 9: Sending notifications

[1787] The notification data generated by the server is sent to the device via a push notification service.

[1788] Input: Notification data

[1789] Data transmission: Use push notification service

[1790] Output: Notification to terminal

[1791] Step 10: Displaying notifications

[1792] The device will notify the user and provide a link to proceed with the sale if necessary.

[1793] Input: Notification data

[1794] Action: Show notification, provide link

[1795] Output: Display a notification to the user

[1796] (Application example 2)

[1797] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1798] Conventional market trend analysis systems send notifications uniformly without considering the user's emotional state, which can result in insufficient notification effectiveness. Furthermore, there is a need for optimal notification content and timing based on the user's emotional state, in addition to information on the optimal time to sell and recommended sales portals. The present invention aims to solve these problems.

[1799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1800] In this invention, the server includes means for users to register desired items, means for saving information on the registered items in a database, means for analyzing market trends based on the saved item information and calculating the optimal time to sell and a recommended selling portal, means for providing an emotion engine that recognizes the emotional state of the user, and means for adjusting the content and timing of notifications based on the emotional state and notifying the user terminal of the analysis results, thereby enabling notifications to be sent at the optimal timing and in the optimal manner according to the user's emotional state.

[1801] The "means for users to register desired items" is an interface that allows users to input the products or assets they have purchased or possess into the system and record that information.

[1802] The "means for saving registered item information in a database" is a processing function that saves the item information entered by the user in a database so that it can be accessed and analyzed later.

[1803] "Means for analyzing market trends based on stored item information and calculating the optimal time to sell and recommended selling portal" refers to algorithms and software that use product information stored in a database to analyze market trends and supply and demand trends, and identify the optimal time to sell and sales platform for the user.

[1804] "Means having an emotion engine that recognizes the user's emotional state" refers to an engine or system that analyzes data such as the user's facial expressions, voice, and body movements in real time to grasp the user's emotional state.

[1805] "Means for adjusting notification content and timing based on emotional state and notifying the user of the analysis results on their device" refers to a function that dynamically adjusts notification content and timing based on data obtained from the emotion engine so that the appropriate notification is sent to the user at the optimal time, and sends a push notification to the user's device.

[1806] This invention combines a system that allows users to register their purchased items, analyzes market trends based on that information, and notifies users of the best time to sell and recommends a sales portal, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1807] System configuration

[1808] The system has five main components:

[1809] 1. User Interface (UI):

[1810] It provides a graphical user interface (GUI) for users to register items. This interface is implemented as a smartphone application, and users can enter detailed information about the purchased items (such as name, purchase date, and purchase price).

[1811] 2. Database:

[1812] A database for storing registered item information is placed on the server, including the product name, purchase date, purchase price, estimated selling value, best time to sell, recommended selling portal, etc.

[1813] 3. Market trend analysis algorithm:

[1814] The server collects market data and uses a specific algorithm to calculate the best time to sell an item and the recommended sales portal. The algorithm uses historical sales data, supply and demand trends, and real-time data from online marketplaces. It uses Python's scikit-learn and pandas, and collects data via an API.

[1815] 4. Emotion Engine:

[1816] The smartphone app is equipped with an emotion engine that collects and analyzes the user's voice data, facial expression data, body movements, etc. in real time. The emotion engine can use the Microsoft Azure Emotion API or Google Cloud Vision API.

[1817] 5. Notification system:

[1818] The server then pushes the analysis results to the user's smartphone. Rich notifications are sent to the user's device using Firebase Cloud Messaging (FCM). The server also adjusts the content and timing of notifications based on data from the emotion engine, ensuring optimal notification timing.

[1819] Processing Details

[1820] 1. Data entry and registration:

[1821] The user uses the smartphone app to input the desired item information, including the item name, purchase date, and purchase price. When the user presses the "Register" button, the app sends the input data in JSON format to the server's API endpoint. The server receives the data and stores it in a database.

[1822] 2. Market Trend Analysis:

[1823] The server periodically collects market data and runs analytical algorithms to calculate the best time to sell and recommend a sales portal based on the stored item information. For example, a script written in Python analyzes past data and predicts future market trends.

[1824] 3. Collecting and analyzing emotional data:

[1825] The emotion engine analyzes the user's voice, facial expressions, and body movements in real time to determine their current emotional state, and this analysis data is periodically updated and sent to the server.

[1826] 4. Notification of Results:

[1827] The server integrates market analysis and sentiment engine data to deliver notifications to users at the optimal time and in the optimal way. Notifications are delivered using Firebase Cloud Messaging (FCM), for example, when a user is in a positive emotional state.

[1828] Specific examples

[1829] For example, if a user purchases a new smartphone, the system works as follows:

[1830] 1. The user opens the app, enters "new smartphone," enters the purchase date and purchase price, and registers.

[1831] 2. The app sends this information to the server, which stores it in a database.

[1832] 3. The server analyzes market trends daily and determines that it would be best to sell on a specific online marketplace in a few months.

[1833] 4. The app collects the user’s emotional data and sends a notification when the user is in a positive state, saying, “The best time to sell this smartphone on a specific online marketplace in the next three months is the expected price of 30,000 yen.”

[1834] 5. The user will receive a notification and can decide whether to proceed with the sale based on the notification. The notification screen will also display a link to proceed with the sale.

[1835] Prompt Sentence Examples

[1836] I recently bought a new smartphone. I'd like to have an app that lets me register my items, track market trends, and notify me when the best time to sell is. It could also use emotion recognition to notify me when I'm in a positive mood.

[1837] The system thus constructed allows users to maximize the value of the products they have purchased, and by providing notifications at optimal times based on the user's emotional state, it allows them to make more effective sales decisions.

[1838] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1839] Step 1:

[1840] Data Entry and Registration

[1841] The user opens the smartphone app and enters the name of the item they purchased, the purchase date, the purchase price, etc. The entered data is converted to JSON format by the device and sent to the server's API endpoint. The server receives this data and stores it in a database. Specifically, the user enters "new smartphone," enters the purchase date and purchase price, and then presses the "Register" button to send the data.

[1842] Input: Item name, purchase date, purchase price

[1843] Output: Item information stored in the database

[1844] Processing: The user enters data, the terminal converts it to JSON format, the server receives the data and saves it to the database

[1845] Step 2:

[1846] Collecting market trend data

[1847] The server periodically collects market data via API, including real-time data from the online market, historical trading data, and supply and demand trends. This data collection is done using scripts written in Python.

[1848] Input: Real-time data obtained from market data API, historical trading data

[1849] Output: Market data stored on the server

[1850] Processing operation: Collect market data through API and store it on the server

[1851] Step 3:

[1852] Market trend analysis

[1853] The server runs an analytical algorithm based on the collected market data. The algorithm uses Python's scikit-learn and pandas to calculate the best time to sell registered items and the recommended sales portal. The algorithm combines historical and real-time data to predict the future value of items.

[1854] Input: Stored market data, product information in the database

[1855] Output: Best time to sell and recommended selling portal

[1856] Processing: Analyzing data with scikit-learn and pandas to predict future market trends

[1857] Step 4:

[1858] Collecting Emotional Data

[1859] The device collects voice data, facial expression data, body movements, etc. in real time to recognize the user's emotional state. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Vision API to analyze this data and identify the user's emotional state.

[1860] Input: User's voice data, facial expression data, body movements

[1861] Output: User's emotional state data

[1862] Processing behavior: Emotion engine analyzes data to identify emotional state

[1863] Step 5:

[1864] Sentiment data analysis and integration

[1865] The collected user emotion data is sent to a server and integrated with market trend analysis results. The server then adjusts the content and timing of notifications based on the user's emotional state. For example, if the user is in a positive state and notifications are effective, the server decides to send notifications.

[1866] Input: User emotional state data, market trend analysis results

[1867] Output: Optimal notification content and timing

[1868] Processing behavior: Analyze emotional state data and adjust notification content and timing

[1869] Step 6:

[1870] Notification of results

[1871] The server then sends push notifications to the user's device via Firebase Cloud Messaging (FCM) using the adjusted notification content and timing, including information such as the best time to sell, recommended selling portals, and estimated prices.

[1872] Input: Optimal notification content and timing

[1873] Output: Push notification to user device

[1874] Process behavior: Send a notification using FCM

[1875] Examples:

[1876] For example, if a user buys a new smartphone, they register it in the app by entering "new smartphone," the purchase date, and the purchase price. The server analyzes market trends daily and determines that "it would be best to sell it on a specific online marketplace in a few months." The app collects the user's emotional data and sends a notification when the user is in a positive state. The notification might say, "It would be best to sell this smartphone on a specific online marketplace in three months, with an expected price of 30,000 yen."

[1877] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1878] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1879] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1880] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1881] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1882] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1883] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1884] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1885] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1886] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1887] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1888] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1889] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1890] 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.

[1891] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1892] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1893] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1894] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1895] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1896] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1897] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1898] The following is further disclosed regarding the above embodiment.

[1899] (Claim 1)

[1900] A means for a user to register a desired item;

[1901] a means for storing information about the registered items in a database;

[1902] A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal;

[1903] A system including a means for notifying a user terminal of the analysis results.

[1904] (Claim 2)

[1905] 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

[1906] (Claim 3)

[1907] 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and the recommended selling portal.

[1908] "Example 1"

[1909] (Claim 1)

[1910] A means for a user to register a desired item;

[1911] a means for storing information about the registered items in a database;

[1912] A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal;

[1913] A means for notifying the user of the analysis results;

[1914] A means of informing users based on appropriate selling times and market trends;

[1915] A means of analyzing market trends using machine learning algorithms;

[1916] A system that includes a means for communicating information to a user using a push notification service.

[1917] (Claim 2)

[1918] 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

[1919] (Claim 3)

[1920] 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and the recommended selling portal.

[1921] "Application Example 1"

[1922] (Claim 1)

[1923] A means for a user to register a desired item;

[1924] a means for storing information about the registered items in a database;

[1925] A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal;

[1926] A means for notifying the user of the analysis results;

[1927] A means for providing push notifications;

[1928] A means for displaying the analysis results on a dedicated screen of the terminal;

[1929] A system including:

[1930] (Claim 2)

[1931] 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

[1932] (Claim 3)

[1933] 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and the recommended selling portal.

[1934] (Claim 4)

[1935] 10. The system of claim 1, further comprising means for calculating an optimal time to sell an item and a recommended selling portal using a specified algorithm upon analyzing market trends.

[1936] (Claim 5)

[1937] The system according to claim 1, characterized in that push notifications are used when notifying the user terminal of the analysis results.

[1938] (Claim 6)

[1939] The system according to claim 1, further comprising means for converting the item information registered by the user into JSON format and transmitting the JSON format to the API endpoint of the server.

[1940] "Example 2: Combining Emotion Engines"

[1941] (Claim 1)

[1942] A means for a user to register a desired item;

[1943] a means for storing information about the registered items in a database;

[1944] A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal;

[1945] A means for notifying the user of the analysis results;

[1946] a means of collecting and analyzing user sentiment data;

[1947] A means to tailor notification content and timing based on emotional data

[1948] A system including:

[1949] (Claim 2)

[1950] 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

[1951] (Claim 3)

[1952] 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and the recommended selling portal.

[1953] "Application example 2 when combining emotion engines"

[1954] (Claim 1)

[1955] A means for a user to register a desired item;

[1956] a means for storing information about the registered items in a database;

[1957] A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal;

[1958] means for providing an emotion engine for recognizing an emotional state of a user;

[1959] A system that includes a means for adjusting the content and timing of notifications based on the user's emotional state and notifying the user of the analysis results on their device.

[1960] (Claim 2)

[1961] 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

[1962] (Claim 3)

[1963] 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and the recommended selling portal. [Explanation of symbols]

[1964] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to register a desired item; a means for storing information about the registered items in a database; A method for analyzing market trends based on stored item information, and calculating the optimal time to sell and the recommended selling portal; A system including a means for notifying a user terminal of the analysis results.

2. 2. The system according to claim 1, further comprising means for recommending the optimal time to purchase an item whose value is increasing based on the registered item information.

3. 2. The system of claim 1, wherein the market trend analysis means uses historical sales data, supply and demand trends, and data obtained from online marketplaces to determine the optimal time to sell and a recommended selling portal.

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