system

Abstract

The system according to this embodiment aims to enable users to easily list items on flea market or auction sites and accurately track their earnings after deducting fees and shipping costs. [Solution] The system according to the embodiment comprises a collection unit, an estimation unit, a calculation unit, a generation unit, and a listing unit. The collection unit collects information on products that users wish to list for sale. The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. The calculation unit calculates the amount of income after deducting fees and shipping costs based on the selling price estimated by the estimation unit and presents it to the user. The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from images based on the amount of income calculated by the calculation unit. The listing unit automatically performs the listing procedure in cooperation with each site based on the listing information generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the work of listing items on flea markets or auction sites is complicated, and it is difficult to accurately grasp the income amount considering fees and shipping costs.

[0005] The system according to the embodiment aims to enable a user to easily list items on flea markets or auction sites and accurately grasp the income amount after deducting fees and shipping costs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an estimation unit, a calculation unit, a generation unit, and a listing unit. The collection unit collects information on products that users wish to list for sale. The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. The calculation unit calculates the amount of revenue after deducting fees and shipping costs based on the selling price estimated by the estimation unit and presents it to the user. The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from images based on the amount of revenue calculated by the calculation unit. The listing unit automatically performs the listing procedure in cooperation with each site based on the listing information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily list items on flea market or auction sites and accurately track their earnings after deducting fees and shipping costs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that searches across flea market and auction sites and answers "how much you can get if you list an item now" after deducting fees and shipping costs. This system works in conjunction with each site and makes maximum use of generation AI to make it easy to list items. It visualizes how users can earn pocket money with little effort, making it a driving force for reuse activities. For example, the system takes the user's input of the item they want to list. For example, the system's generation AI searches across various flea market and auction sites and estimates the selling price of the item based on past and current sales data. Furthermore, the system calculates "how much you can get if you list an item now" after deducting fees and shipping costs and presents it to the user. Next, the system's generation AI generates listing information such as titles, product descriptions, and categories that differ for each site from the image. This allows users to easily create listing information. For example, the system's generation AI analyzes the product image and automatically generates appropriate titles and product descriptions. Furthermore, the system works in conjunction with each site, and the generation AI automatically handles the listing process. This allows users to list items without any hassle. For example, the system uses a generating AI to input the necessary information into each site's listing form and complete the listing. This system makes it visible to users that they can earn some pocket money with little effort, thus motivating reuse activities. For instance, the system allows users to easily list unwanted items when sorting through belongings or decluttering, and earn income. The system also allows for the effective use of items that are troublesome to throw away or that feel wasteful to discard. By utilizing a generating AI, this system allows users to easily create listing information and complete the listing process, thus promoting the use of flea market and auction sites. This will revitalize reuse activities and create a world without waste.

[0029] The system according to this embodiment comprises a collection unit, an estimation unit, a calculation unit, a generation unit, and a listing unit. The collection unit collects information on products that users wish to list for sale. The collection unit, for example, stores product information entered by the user in a database. The collection unit can also, for example, collect images of products taken by the user with a smartphone camera. The collection unit can also, for example, collect detailed product information entered by the user (e.g., product name, category, condition, etc.). The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. The estimation unit, for example, estimates the current market price based on past sales data. The estimation unit can also, for example, analyze the sales history of each site and calculate the average selling price of the product. The estimation unit can also, for example, estimate the selling price by considering the balance of supply and demand for the product. The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the selling price estimated by the estimation unit and presents it to the user. The calculation unit calculates the revenue amount by considering the fee rate of each site. The calculation unit can, for example, calculate the revenue amount considering the shipping costs of the product. The calculation unit can also, for example, provide an interface for presenting the revenue amount to the user. The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from images based on the revenue amount calculated by the calculation unit. The generation unit can, for example, analyze product images and generate an appropriate title. The generation unit can also, for example, analyze product images and generate a detailed product description. The generation unit can also, for example, automatically select a product category. The listing unit automatically performs the listing procedure in cooperation with each site based on the listing information generated by the generation unit. The listing unit can, for example, input the necessary information into the listing form of each site. The listing unit can also, for example, display a confirmation message to complete the listing procedure. The listing unit can also, for example, notify the user of the progress of the listing procedure. As a result, the system according to the embodiment allows users to easily create listing information and perform the listing procedure.

[0030] The data collection unit collects information about products that users wish to list for sale. For example, the data collection unit stores the product information entered by the user in a database. Specifically, it collects detailed information such as the product name, category, condition, and desired price entered by the user through a dedicated input form. This information is stored in the database and used for subsequent processing. The data collection unit can also collect images of products taken by users with their smartphone cameras. Images are collected through a dedicated upload function and undergo pre-processing for image analysis. For example, the resolution and format of images are standardized, and processing such as noise reduction and color correction is applied. Furthermore, the data collection unit can also collect detailed product information entered by the user (e.g., product name, category, condition, etc.). This allows the data collection unit to centrally manage the diverse information provided by users and improve the accuracy and efficiency of the entire system. The data collection unit also has an input content checking function to ensure the accuracy of the information provided by users. For example, it implements a validation function to prevent missing required fields or the input of inappropriate values. This allows the data collection unit to improve the quality of information provided by users and ensure that subsequent processing proceeds smoothly.

[0031] The estimation unit analyzes sales data from various flea market and auction sites based on information collected by the collection unit and estimates the selling price. For example, the estimation unit estimates the current market price based on past sales data. Specifically, it accesses the sales history database of each site and analyzes the prices at which similar products have been traded in the past. The estimation unit can also, for example, analyze the sales history of each site and calculate the average selling price of a product. This involves analyzing price distributions and trends using statistical methods and machine learning algorithms. Furthermore, the estimation unit can also estimate the selling price by considering the balance of supply and demand for the product. For example, if the demand for a particular product is high or the supply is insufficient, the price tends to rise, so the unit adjusts the price considering these factors. The estimation unit utilizes AI to analyze the collected information from multiple angles and derive the optimal selling price. For example, it can use natural language processing technology to analyze product descriptions and reviews and evaluate the value of the product. This allows the estimation unit to provide users with reliable pricing information and increase the success rate of listings.

[0032] The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the sales price estimated by the estimation unit, and presents it to the user. For example, the calculation unit calculates the revenue amount considering the fee rates of each site. Specifically, it registers the fee rates of each flea market and auction site in a database and performs a calculation by subtracting the fees from the estimated sales price. The calculation unit can also calculate the revenue amount considering, for example, the shipping costs of the product. Since shipping costs vary depending on the size and weight of the product and the destination region, it calculates the accurate shipping cost considering these factors. The calculation unit can also provide, for example, an interface to present the revenue amount to the user. Through the interface provided by the calculation unit, the user can check the estimated revenue amount and decide whether or not to list the item. Furthermore, the calculation unit also has a revenue amount simulation function, allowing the user to find the optimal listing conditions by trying different sales prices and shipping conditions. In this way, the calculation unit can provide users with highly transparent information and support their decision-making regarding listing items.

[0033] The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site, based on the revenue amount calculated by the calculation unit. For example, the generation unit analyzes product images and generates appropriate titles. Specifically, it uses image recognition technology to extract product features and automatically generates attractive titles based on them. The generation unit can also analyze product images and generate detailed product descriptions. It uses natural language generation technology to create descriptions that describe the product's features, condition, and usability in detail. The generation unit can also automatically select product categories. Based on the results of image and text analysis, it selects the optimal category and reflects it in the listing information. The generation unit utilizes AI to automatically generate listing information suitable for each site's format based on information provided by the user. This allows users to create high-quality listing information without much effort. Furthermore, the generation unit generates information that complies with each site's listing guidelines and policies, supporting a smooth listing process. In this way, the generation unit reduces the burden on users and increases the success rate of listings.

[0034] The listing unit automatically performs the listing process in conjunction with each site, based on the listing information generated by the generation unit. For example, the listing unit inputs the necessary information into the listing form of each site. Specifically, it automatically inputs generated information such as title, product description, category, and price into the listing form of each site and proceeds with the listing process. The listing unit can also display a confirmation message to complete the listing process. Through the confirmation message, the user can make a final check of the listing details and make corrections as needed. The listing unit can also notify the user of the progress of the listing process. When the listing process is complete, it sends a notification to the user informing them that the listing was successful. Furthermore, the listing unit can monitor the listing status of each site and update or correct the listing information as needed. This allows the listing unit to make the listing process easy for users and increase the success rate of listings. The listing unit utilizes the APIs of each site to automate the listing process and significantly reduce the effort required from the user. This allows the listing unit to provide users with a fast and efficient listing process and support the success of their listings.

[0035] The data collection unit can analyze the user's past listing history and select the optimal information collection method. For example, the data collection unit can prioritize selecting listing methods that the user has succeeded with in the past. The data collection unit can also avoid listing methods that the user has failed with in the past. For example, the data collection unit can suggest the most effective information collection method based on the user's past listing history. In this way, the optimal information collection method can be selected by analyzing past listing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past listing history data into a generating AI and have the generating AI select the optimal information collection method.

[0036] The data collection unit can filter product information based on the user's current areas of interest and trends. For example, the data collection unit can prioritize collecting product information in categories that the user is currently interested in. The data collection unit can also filter product information based on trends that the user is following. For example, the data collection unit can prioritize collecting product information related to the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's areas of interest and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user areas of interest and trend data into a generating AI and have the generating AI perform the information filtering.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product information. For example, the data collection unit can prioritize the collection of information on products close to the user's current location. The data collection unit can also collect highly relevant information based on the user's past travel history. For example, the data collection unit can suggest optimal product information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the information collection.

[0038] The data collection unit can analyze the user's social media activity and collect relevant information when collecting product information. For example, the data collection unit can prioritize collecting product information that the user has shown interest in on social media. For example, the data collection unit can also collect product information that the user's social media followers are interested in. For example, the data collection unit can analyze the user's social media activity and suggest the most suitable product information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0039] The estimation unit can improve the accuracy of its estimations by referring to past sales data when estimating sales prices. For example, the estimation unit estimates the current market price based on past sales data. The estimation unit can also estimate by considering seasonal price fluctuations from past sales data. The estimation unit can also analyze past sales data to estimate the most appropriate price. This allows for improved estimation accuracy by referring to past sales data. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input past sales data into a generating AI and have the generating AI perform improvements to the accuracy of price estimation.

[0040] The estimation unit can apply different estimation algorithms to each product category when estimating the selling price. For example, in the case of electronic devices, the estimation unit estimates the price by considering technical specifications. For example, in the case of clothing, the estimation unit can also estimate the price by considering the brand and condition. For example, in the case of books, the estimation unit can also estimate the price by considering rarity and demand. By applying different estimation algorithms to each product category, more accurate price estimation can be achieved. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input product category data into a generating AI and have the generating AI execute the application of the price estimation algorithm.

[0041] The estimation unit can determine the estimation priority based on the product submission date when estimating the selling price. For example, the estimation unit may prioritize products that have been submitted earlier. The estimation unit may also postpone products that have been submitted later. The estimation unit may also dynamically adjust the estimation priority based on the submission date. This enables efficient estimation by determining the estimation priority based on the product submission date. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input product submission date data into a generating AI and have the generating AI perform the determination of the estimation priority.

[0042] The estimation unit can improve the accuracy of its estimations by referring to relevant market data for the product when estimating the selling price. For example, the estimation unit estimates the current market price based on relevant market data. The estimation unit can also estimate by considering the balance of supply and demand from relevant market data. The estimation unit can also analyze relevant market data and estimate the most appropriate price. This improves the accuracy of the estimation by referring to relevant market data. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant market data into a generating AI and have the generating AI perform improvements to the accuracy of price estimation.

[0043] The calculation unit can improve the accuracy of its calculations by referring to past commission and shipping fee data when calculating revenue. For example, the calculation unit can calculate revenue based on past commission data, taking current commissions into consideration. The calculation unit can also calculate revenue based on past shipping fee data, taking current shipping fees into consideration. The calculation unit can also analyze past commission and shipping fee data to calculate the most appropriate revenue amount. This allows for improved calculation accuracy by referring to past commission and shipping fee data. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past commission and shipping fee data into a generating AI and have the generating AI perform the task of improving the accuracy of revenue calculation.

[0044] The calculation unit can apply different calculation algorithms to each product category when calculating revenue. For example, in the case of electronic devices, the calculation unit calculates revenue considering technical specifications. For example, in the case of clothing, the calculation unit can also calculate revenue considering brand and condition. For example, in the case of books, the calculation unit can also calculate revenue considering rarity and demand. By applying different calculation algorithms to each product category, a more accurate revenue can be calculated. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input product category data into a generating AI and have the generating AI perform the revenue calculation.

[0045] The calculation unit can determine the calculation priority based on the product submission date when calculating the revenue amount. For example, the calculation unit may prioritize products submitted earlier. For example, the calculation unit may also postpone products submitted later. For example, the calculation unit may dynamically adjust the calculation priority based on the submission date. This allows for efficient calculation by determining the calculation priority based on the product submission date. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input product submission date data into a generating AI and have the generating AI perform the calculation priority determination.

[0046] The calculation unit can improve the accuracy of its calculations by referring to relevant market data for the product when calculating revenue. For example, the calculation unit can calculate revenue by considering the current market price based on relevant market data. The calculation unit can also calculate revenue by considering the balance of supply and demand from relevant market data. The calculation unit can also analyze relevant market data and calculate the most appropriate revenue. This improves the accuracy of the calculations by referring to relevant market data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input relevant market data into a generating AI and have the generating AI perform the task of improving the accuracy of revenue calculations.

[0047] The generation unit can improve the accuracy of its generation of listing information by referring to the product's image analysis data. For example, the generation unit can generate an appropriate title based on the product's image analysis data. The generation unit can also generate a detailed product description based on the product's image analysis data. The generation unit can also select the optimal category based on the product's image analysis data. By referring to the image analysis data, the accuracy of the generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the product's image analysis data into a generation AI and have the generation AI perform the generation of listing information.

[0048] The generation unit can apply different generation algorithms to each product category when generating listing information. For example, in the case of electronic devices, the generation unit can generate listing information that emphasizes technical specifications. For example, in the case of clothing, the generation unit can also generate listing information that emphasizes brand and condition. For example, in the case of books, the generation unit can also generate listing information that emphasizes rarity and demand. By applying different generation algorithms to each product category, more accurate listing information can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product category data into a generation AI and have the generation AI perform the generation of listing information.

[0049] The generation unit can determine the generation priority based on the product submission date when generating listing information. For example, the generation unit may prioritize generating products with earlier submission dates. The generation unit may also postpone generating products with later submission dates. The generation unit may also dynamically adjust the generation priority based on the submission date. This enables efficient generation by determining the generation priority based on the product submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product submission date data into the generation AI and have the generation AI determine the generation priority.

[0050] The generation unit can improve the accuracy of its listing information generation by referring to relevant market data for the product. For example, the generation unit can generate an appropriate title based on relevant market data. The generation unit can also generate a detailed product description based on relevant market data. The generation unit can also select the optimal category based on relevant market data. In this way, the accuracy of the generation can be improved by referring to relevant market data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevant market data into a generation AI and have the generation AI perform the generation of listing information.

[0051] The listing department can improve the accuracy of the listing process by referring to past listing data during the listing process. For example, the listing department can optimize the current procedure based on past listing data. For example, the listing department can select the most effective procedure from past listing data. For example, the listing department can analyze past listing data and propose the optimal procedure. In this way, the accuracy of the procedure can be improved by referring to past listing data. Some or all of the above processes in the listing department may be performed using AI, for example, or without AI. For example, the listing department can input past listing data into a generating AI and have the generating AI perform the procedure accuracy improvement.

[0052] The listing department can apply different procedural algorithms to each product category during the listing process. For example, in the case of electronic devices, the listing department may perform a procedure that emphasizes technical specifications. For example, in the case of clothing, the listing department may perform a procedure that emphasizes brand and condition. For example, in the case of books, the listing department may perform a procedure that emphasizes rarity and demand. By applying different procedural algorithms to each product category, more accurate listings can be performed. Some or all of the above processing in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input product category data into a generating AI and have the generating AI perform the procedure.

[0053] The listing department can determine the priority of the listing process based on the submission date of the products. For example, the listing department can prioritize products submitted earlier. For example, the listing department can also postpone products submitted later. For example, the listing department can dynamically adjust the priority of the process based on the submission date. This allows for efficient processing by determining the priority of the process based on the submission date of the products. Some or all of the above processing in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input product submission date data into a generating AI and have the generating AI perform the determination of the priority of the process.

[0054] The listing department can improve the accuracy of the listing process by referring to relevant market data for the product. For example, the listing department can select the optimal procedure based on relevant market data. For example, the listing department can also suggest the most effective procedure from relevant market data. For example, the listing department can analyze relevant market data to improve the accuracy of the procedure. In this way, the accuracy of the procedure can be improved by referring to relevant market data. Some or all of the above processes in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input relevant market data into a generating AI and have the generating AI perform the procedure accuracy improvement.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's purchase history and recommend products to list for sale. For example, the data collection unit can prioritize recommending products similar to those the user has previously purchased. For example, the data collection unit can also recommend products in categories that the user frequently purchases. For example, the data collection unit can recommend products to list for sale based on the user's ratings of purchased products. This allows for the listing of more appropriate products by analyzing the user's purchase history.

[0057] The estimation unit can analyze a user's past listing history and estimate the optimal selling price. For example, the estimation unit can refer to the user's past successful selling prices. It can also avoid selling prices that the user has previously failed at. Furthermore, it can suggest the most effective selling price based on the user's past listing history. In this way, the optimal selling price can be estimated by analyzing past listing history.

[0058] The calculation unit can analyze the user's past income data to improve the accuracy of income calculations. For example, the calculation unit can estimate current income based on past income data. The calculation unit can also take into account seasonal fluctuations in income from past income data. For example, the calculation unit can analyze past income data to calculate the most appropriate income. This allows for improved calculation accuracy by referencing past income data.

[0059] The generation unit can analyze the user's past listing information and improve the accuracy of listing information generation. For example, the generation unit generates current listing information based on past listing information. For example, the generation unit can also generate the most effective listing information from past listing information. For example, the generation unit can analyze past listing information and suggest the optimal listing information. In this way, the accuracy of generation can be improved by referring to past listing information.

[0060] The listing department can analyze users' past listing procedure data to improve the accuracy of the procedure. For example, the listing department can optimize the current procedure based on past listing procedure data. For example, the listing department can select the most effective procedure from past listing procedure data. For example, the listing department can analyze past listing procedure data and propose the optimal procedure. In this way, the accuracy of the procedure can be improved by referring to past listing procedure data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects information about products that users want to list for sale. For example, it can save product information entered by the user to a database, collect product images taken with a smartphone camera, and collect detailed product information (product name, category, condition, etc.). Step 2: The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. For example, it can estimate the current market price based on past sales data, calculate the average selling price of a product by analyzing the sales history of each site, or estimate the selling price by considering the balance of supply and demand for the product. Step 3: The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the sales price estimated by the estimation unit, and presents it to the user. For example, it can calculate the revenue amount considering the commission rates of each site and the shipping costs of the products, and provide an interface to present the revenue amount to the user. Step 4: The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site, based on the revenue amount calculated by the calculation unit. For example, it can analyze product images to generate appropriate titles and detailed product descriptions, or automatically select product categories. Step 5: The listing unit automatically performs the listing process in conjunction with each site based on the listing information generated by the generation unit. For example, it can input the necessary information into each site's listing form, display confirmation messages to complete the listing process, and notify the user of the progress of the listing process.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that searches across flea market and auction sites and answers "how much you can get if you list an item now" after deducting fees and shipping costs. This system works in conjunction with each site and makes maximum use of generation AI to make it easy to list items. It visualizes how users can earn pocket money with little effort, making it a driving force for reuse activities. For example, the system takes the user's input of the item they want to list. For example, the system's generation AI searches across various flea market and auction sites and estimates the selling price of the item based on past and current sales data. Furthermore, the system calculates "how much you can get if you list an item now" after deducting fees and shipping costs and presents it to the user. Next, the system's generation AI generates listing information such as titles, product descriptions, and categories that differ for each site from the image. This allows users to easily create listing information. For example, the system's generation AI analyzes the product image and automatically generates appropriate titles and product descriptions. Furthermore, the system works in conjunction with each site, and the generation AI automatically handles the listing process. This allows users to list items without any hassle. For example, the system uses a generating AI to input the necessary information into each site's listing form and complete the listing. This system makes it visible to users that they can earn some pocket money with little effort, thus motivating reuse activities. For instance, the system allows users to easily list unwanted items when sorting through belongings or decluttering, and earn income. The system also allows for the effective use of items that are troublesome to throw away or that feel wasteful to discard. By utilizing a generating AI, this system allows users to easily create listing information and complete the listing process, thus promoting the use of flea market and auction sites. This will revitalize reuse activities and create a world without waste.

[0064] The system according to this embodiment comprises a collection unit, an estimation unit, a calculation unit, a generation unit, and a listing unit. The collection unit collects information on products that users wish to list for sale. The collection unit, for example, stores product information entered by the user in a database. The collection unit can also, for example, collect images of products taken by the user with a smartphone camera. The collection unit can also, for example, collect detailed product information entered by the user (e.g., product name, category, condition, etc.). The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. The estimation unit, for example, estimates the current market price based on past sales data. The estimation unit can also, for example, analyze the sales history of each site and calculate the average selling price of the product. The estimation unit can also, for example, estimate the selling price by considering the balance of supply and demand for the product. The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the selling price estimated by the estimation unit and presents it to the user. The calculation unit calculates the revenue amount by considering the fee rate of each site. The calculation unit can, for example, calculate the revenue amount considering the shipping costs of the product. The calculation unit can also, for example, provide an interface for presenting the revenue amount to the user. The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from images based on the revenue amount calculated by the calculation unit. The generation unit can, for example, analyze product images and generate an appropriate title. The generation unit can also, for example, analyze product images and generate a detailed product description. The generation unit can also, for example, automatically select a product category. The listing unit automatically performs the listing procedure in cooperation with each site based on the listing information generated by the generation unit. The listing unit can, for example, input the necessary information into the listing form of each site. The listing unit can also, for example, display a confirmation message to complete the listing procedure. The listing unit can also, for example, notify the user of the progress of the listing procedure. As a result, the system according to the embodiment allows users to easily create listing information and perform the listing procedure.

[0065] The data collection unit collects information about products that users wish to list for sale. For example, the data collection unit stores the product information entered by the user in a database. Specifically, it collects detailed information such as the product name, category, condition, and desired price entered by the user through a dedicated input form. This information is stored in the database and used for subsequent processing. The data collection unit can also collect images of products taken by users with their smartphone cameras. Images are collected through a dedicated upload function and undergo pre-processing for image analysis. For example, the resolution and format of images are standardized, and processing such as noise reduction and color correction is applied. Furthermore, the data collection unit can also collect detailed product information entered by the user (e.g., product name, category, condition, etc.). This allows the data collection unit to centrally manage the diverse information provided by users and improve the accuracy and efficiency of the entire system. The data collection unit also has an input content checking function to ensure the accuracy of the information provided by users. For example, it implements a validation function to prevent missing required fields or the input of inappropriate values. This allows the data collection unit to improve the quality of information provided by users and ensure that subsequent processing proceeds smoothly.

[0066] The estimation unit analyzes sales data from various flea market and auction sites based on information collected by the collection unit and estimates the selling price. For example, the estimation unit estimates the current market price based on past sales data. Specifically, it accesses the sales history database of each site and analyzes the prices at which similar products have been traded in the past. The estimation unit can also, for example, analyze the sales history of each site and calculate the average selling price of a product. This involves analyzing price distributions and trends using statistical methods and machine learning algorithms. Furthermore, the estimation unit can also estimate the selling price by considering the balance of supply and demand for the product. For example, if the demand for a particular product is high or the supply is insufficient, the price tends to rise, so the unit adjusts the price considering these factors. The estimation unit utilizes AI to analyze the collected information from multiple angles and derive the optimal selling price. For example, it can use natural language processing technology to analyze product descriptions and reviews and evaluate the value of the product. This allows the estimation unit to provide users with reliable pricing information and increase the success rate of listings.

[0067] The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the sales price estimated by the estimation unit, and presents it to the user. For example, the calculation unit calculates the revenue amount considering the fee rates of each site. Specifically, it registers the fee rates of each flea market and auction site in a database and performs a calculation by subtracting the fees from the estimated sales price. The calculation unit can also calculate the revenue amount considering, for example, the shipping costs of the product. Since shipping costs vary depending on the size and weight of the product and the destination region, it calculates the accurate shipping cost considering these factors. The calculation unit can also provide, for example, an interface to present the revenue amount to the user. Through the interface provided by the calculation unit, the user can check the estimated revenue amount and decide whether or not to list the item. Furthermore, the calculation unit also has a revenue amount simulation function, allowing the user to find the optimal listing conditions by trying different sales prices and shipping conditions. In this way, the calculation unit can provide users with highly transparent information and support their decision-making regarding listing items.

[0068] The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site, based on the revenue amount calculated by the calculation unit. For example, the generation unit analyzes product images and generates appropriate titles. Specifically, it uses image recognition technology to extract product features and automatically generates attractive titles based on them. The generation unit can also analyze product images and generate detailed product descriptions. It uses natural language generation technology to create descriptions that describe the product's features, condition, and usability in detail. The generation unit can also automatically select product categories. Based on the results of image and text analysis, it selects the optimal category and reflects it in the listing information. The generation unit utilizes AI to automatically generate listing information suitable for each site's format based on information provided by the user. This allows users to create high-quality listing information without much effort. Furthermore, the generation unit generates information that complies with each site's listing guidelines and policies, supporting a smooth listing process. In this way, the generation unit reduces the burden on users and increases the success rate of listings.

[0069] The listing unit automatically performs the listing process in conjunction with each site, based on the listing information generated by the generation unit. For example, the listing unit inputs the necessary information into the listing form of each site. Specifically, it automatically inputs generated information such as title, product description, category, and price into the listing form of each site and proceeds with the listing process. The listing unit can also display a confirmation message to complete the listing process. Through the confirmation message, the user can make a final check of the listing details and make corrections as needed. The listing unit can also notify the user of the progress of the listing process. When the listing process is complete, it sends a notification to the user informing them that the listing was successful. Furthermore, the listing unit can monitor the listing status of each site and update or correct the listing information as needed. This allows the listing unit to make the listing process easy for users and increase the success rate of listings. The listing unit utilizes the APIs of each site to automate the listing process and significantly reduce the effort required from the user. This allows the listing unit to provide users with a fast and efficient listing process and support the success of their listings.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of product information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect information immediately. For example, if the user is stressed, the data collection unit can postpone information collection. For example, if the user is busy, the data collection unit can adjust the information collection to fit the user's schedule. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze the user's past listing history and select the optimal information collection method. For example, the data collection unit can prioritize selecting listing methods that the user has succeeded with in the past. The data collection unit can also avoid listing methods that the user has failed with in the past. For example, the data collection unit can suggest the most effective information collection method based on the user's past listing history. In this way, the optimal information collection method can be selected by analyzing past listing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past listing history data into a generating AI and have the generating AI select the optimal information collection method.

[0072] The data collection unit can filter product information based on the user's current areas of interest and trends. For example, the data collection unit can prioritize collecting product information in categories that the user is currently interested in. The data collection unit can also filter product information based on trends that the user is following. For example, the data collection unit can prioritize collecting product information related to the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's areas of interest and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user areas of interest and trend data into a generating AI and have the generating AI perform the information filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of items to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may set a higher priority for items to collect. For example, if the user is calm, the data collection unit may set a lower priority for items to collect. For example, if the user is in a hurry, the data collection unit may immediately determine the priority of items to collect. This allows information to be collected in a more appropriate order by determining the priority of items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of item priorities.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product information. For example, the data collection unit can prioritize the collection of information on products close to the user's current location. The data collection unit can also collect highly relevant information based on the user's past travel history. For example, the data collection unit can suggest optimal product information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the information collection.

[0075] The data collection unit can analyze the user's social media activity and collect relevant information when collecting product information. For example, the data collection unit can prioritize collecting product information that the user has shown interest in on social media. For example, the data collection unit can also collect product information that the user's social media followers are interested in. For example, the data collection unit can analyze the user's social media activity and suggest the most suitable product information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0076] The estimation unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, the estimation unit can perform a detailed price estimate. For example, if the user is in a hurry, the estimation unit can also perform a simplified price estimate. For example, if the user is excited, the estimation unit can quickly present the price estimation results. This allows for more appropriate price estimation by adjusting the estimation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the estimation unit may be performed using AI or not using AI. For example, the estimation unit can input user emotion data into the generative AI and have the generative AI adjust the pricing method.

[0077] The estimation unit can improve the accuracy of its estimations by referring to past sales data when estimating sales prices. For example, the estimation unit estimates the current market price based on past sales data. The estimation unit can also estimate by considering seasonal price fluctuations from past sales data. The estimation unit can also analyze past sales data to estimate the most appropriate price. This allows for improved estimation accuracy by referring to past sales data. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input past sales data into a generating AI and have the generating AI perform improvements to the accuracy of price estimation.

[0078] The estimation unit can apply different estimation algorithms to each product category when estimating the selling price. For example, in the case of electronic devices, the estimation unit estimates the price by considering technical specifications. For example, in the case of clothing, the estimation unit can also estimate the price by considering the brand and condition. For example, in the case of books, the estimation unit can also estimate the price by considering rarity and demand. By applying different estimation algorithms to each product category, more accurate price estimation can be achieved. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input product category data into a generating AI and have the generating AI execute the application of the price estimation algorithm.

[0079] The estimation unit can estimate the user's emotions and adjust how the estimation results are displayed based on the estimated emotions. For example, if the user is relaxed, the estimation unit can display detailed estimation results. If the user is in a hurry, the estimation unit can also display concise estimation results. If the user is excited, the estimation unit can also display visually appealing estimation results. This allows for a more appropriate display by adjusting how the estimation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using AI, or not using AI. For example, the estimation unit can input user emotion data into the generative AI and have the generative AI adjust how the estimation results are displayed.

[0080] The estimation unit can determine the estimation priority based on the product submission date when estimating the selling price. For example, the estimation unit may prioritize products that have been submitted earlier. The estimation unit may also postpone products that have been submitted later. The estimation unit may also dynamically adjust the estimation priority based on the submission date. This enables efficient estimation by determining the estimation priority based on the product submission date. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input product submission date data into a generating AI and have the generating AI perform the determination of the estimation priority.

[0081] The estimation unit can improve the accuracy of its estimations by referring to relevant market data for the product when estimating the selling price. For example, the estimation unit estimates the current market price based on relevant market data. The estimation unit can also estimate by considering the balance of supply and demand from relevant market data. The estimation unit can also analyze relevant market data and estimate the most appropriate price. This improves the accuracy of the estimation by referring to relevant market data. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant market data into a generating AI and have the generating AI perform improvements to the accuracy of price estimation.

[0082] The calculation unit can estimate the user's emotions and adjust the method of calculating the income amount based on the estimated user emotions. For example, if the user is relaxed, the calculation unit can calculate a detailed income amount. For example, if the user is in a hurry, the calculation unit can also calculate a simplified income amount. For example, if the user is excited, the calculation unit can also quickly present the income calculation result. This allows for the calculation of a more appropriate income amount by adjusting the calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI adjust the method of calculating the income amount.

[0083] The calculation unit can improve the accuracy of its calculations by referring to past commission and shipping fee data when calculating revenue. For example, the calculation unit can calculate revenue based on past commission data, taking current commissions into consideration. The calculation unit can also calculate revenue based on past shipping fee data, taking current shipping fees into consideration. The calculation unit can also analyze past commission and shipping fee data to calculate the most appropriate revenue amount. This allows for improved calculation accuracy by referring to past commission and shipping fee data. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past commission and shipping fee data into a generating AI and have the generating AI perform the task of improving the accuracy of revenue calculation.

[0084] The calculation unit can apply different calculation algorithms to each product category when calculating revenue. For example, in the case of electronic devices, the calculation unit calculates revenue considering technical specifications. For example, in the case of clothing, the calculation unit can also calculate revenue considering brand and condition. For example, in the case of books, the calculation unit can also calculate revenue considering rarity and demand. By applying different calculation algorithms to each product category, a more accurate revenue can be calculated. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input product category data into a generating AI and have the generating AI perform the revenue calculation.

[0085] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated user emotions. For example, if the user is relaxed, the calculation unit can display detailed calculation results. For example, if the user is in a hurry, the calculation unit can also display concise calculation results. For example, if the user is excited, the calculation unit can also display visually appealing calculation results. This allows for a more appropriate display by adjusting the display method of the calculation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the calculation results.

[0086] The calculation unit can determine the calculation priority based on the product submission date when calculating the revenue amount. For example, the calculation unit may prioritize products submitted earlier. For example, the calculation unit may also postpone products submitted later. For example, the calculation unit may dynamically adjust the calculation priority based on the submission date. This allows for efficient calculation by determining the calculation priority based on the product submission date. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input product submission date data into a generating AI and have the generating AI perform the calculation priority determination.

[0087] The calculation unit can improve the accuracy of its calculations by referring to relevant market data for the product when calculating revenue. For example, the calculation unit can calculate revenue by considering the current market price based on relevant market data. The calculation unit can also calculate revenue by considering the balance of supply and demand from relevant market data. The calculation unit can also analyze relevant market data and calculate the most appropriate revenue. This improves the accuracy of the calculations by referring to relevant market data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input relevant market data into a generating AI and have the generating AI perform the task of improving the accuracy of revenue calculations.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating listing information based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed listing information. For example, if the user is in a hurry, the generation unit can also generate simple listing information. For example, if the user is excited, the generation unit can also generate visually appealing listing information. In this way, by adjusting the generation method according to the user's emotions, more appropriate listing information can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the method of generating listing information.

[0089] The generation unit can improve the accuracy of its generation of listing information by referring to the product's image analysis data. For example, the generation unit can generate an appropriate title based on the product's image analysis data. The generation unit can also generate a detailed product description based on the product's image analysis data. The generation unit can also select the optimal category based on the product's image analysis data. By referring to the image analysis data, the accuracy of the generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the product's image analysis data into a generation AI and have the generation AI perform the generation of listing information.

[0090] The generation unit can apply different generation algorithms to each product category when generating listing information. For example, in the case of electronic devices, the generation unit can generate listing information that emphasizes technical specifications. For example, in the case of clothing, the generation unit can also generate listing information that emphasizes brand and condition. For example, in the case of books, the generation unit can also generate listing information that emphasizes rarity and demand. By applying different generation algorithms to each product category, more accurate listing information can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product category data into a generation AI and have the generation AI perform the generation of listing information.

[0091] The generation unit can estimate the user's emotions and adjust how the generated results are displayed based on the estimated emotions. For example, if the user is relaxed, the generation unit can display detailed results. If the user is in a hurry, the generation unit can also display concise results. If the user is excited, the generation unit can also display visually appealing results. By adjusting how the generated results are displayed according to the user's emotions, a more appropriate display can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust how the generated results are displayed.

[0092] The generation unit can determine the generation priority based on the product submission date when generating listing information. For example, the generation unit may prioritize generating products with earlier submission dates. The generation unit may also postpone generating products with later submission dates. The generation unit may also dynamically adjust the generation priority based on the submission date. This enables efficient generation by determining the generation priority based on the product submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product submission date data into the generation AI and have the generation AI determine the generation priority.

[0093] The generation unit can improve the accuracy of its listing information generation by referring to relevant market data for the product. For example, the generation unit can generate an appropriate title based on relevant market data. The generation unit can also generate a detailed product description based on relevant market data. The generation unit can also select the optimal category based on relevant market data. In this way, the accuracy of the generation can be improved by referring to relevant market data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relevant market data into a generation AI and have the generation AI perform the generation of listing information.

[0094] The listing unit can estimate the user's emotions and adjust the listing procedure based on the estimated emotions. For example, if the user is relaxed, the listing unit can perform a detailed listing procedure. If the user is in a hurry, the listing unit can perform a simplified listing procedure. If the user is excited, the listing unit can perform a rapid listing procedure. This allows for a more appropriate procedure by adjusting the listing procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit can input user emotion data into a generative AI and have the generative AI adjust the listing procedure.

[0095] The listing department can improve the accuracy of the listing process by referring to past listing data during the listing process. For example, the listing department can optimize the current procedure based on past listing data. For example, the listing department can select the most effective procedure from past listing data. For example, the listing department can analyze past listing data and propose the optimal procedure. In this way, the accuracy of the procedure can be improved by referring to past listing data. Some or all of the above processes in the listing department may be performed using AI, for example, or without AI. For example, the listing department can input past listing data into a generating AI and have the generating AI perform the procedure accuracy improvement.

[0096] The listing department can apply different procedural algorithms to each product category during the listing process. For example, in the case of electronic devices, the listing department may perform a procedure that emphasizes technical specifications. For example, in the case of clothing, the listing department may perform a procedure that emphasizes brand and condition. For example, in the case of books, the listing department may perform a procedure that emphasizes rarity and demand. By applying different procedural algorithms to each product category, more accurate listings can be performed. Some or all of the above processing in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input product category data into a generating AI and have the generating AI perform the procedure.

[0097] The listing unit can estimate the user's emotions and determine the priority of the listing process based on the estimated emotions. For example, if the user is relaxed, the listing unit will set a high priority for the listing process. For example, if the user is in a hurry, the listing unit may set a low priority for the listing process. For example, if the user is excited, the listing unit may immediately determine the priority of the listing process. This allows the process to be carried out in a more appropriate order by determining the priority of the listing process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using AI, for example, or not using AI. For example, the listing unit can input user emotion data into a generative AI and have the generative AI determine the priority of the listing process.

[0098] The listing department can determine the priority of the listing process based on the submission date of the products. For example, the listing department can prioritize products submitted earlier. For example, the listing department can also postpone products submitted later. For example, the listing department can dynamically adjust the priority of the process based on the submission date. This allows for efficient processing by determining the priority of the process based on the submission date of the products. Some or all of the above processing in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input product submission date data into a generating AI and have the generating AI perform the determination of the priority of the process.

[0099] The listing department can improve the accuracy of the listing process by referring to relevant market data for the product. For example, the listing department can select the optimal procedure based on relevant market data. For example, the listing department can also suggest the most effective procedure from relevant market data. For example, the listing department can analyze relevant market data to improve the accuracy of the procedure. In this way, the accuracy of the procedure can be improved by referring to relevant market data. Some or all of the above processes in the listing department may be performed using AI, for example, or not using AI. For example, the listing department can input relevant market data into a generating AI and have the generating AI perform the procedure accuracy improvement.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can analyze a user's purchase history and recommend products to list for sale. For example, the data collection unit can prioritize recommending products similar to those the user has previously purchased. For example, the data collection unit can also recommend products in categories that the user frequently purchases. For example, the data collection unit can recommend products to list for sale based on the user's ratings of purchased products. This allows for the listing of more appropriate products by analyzing the user's purchase history.

[0102] The data collection unit can estimate the user's emotions and suggest items to list based on those emotions. For example, if the user is relaxed, the data collection unit will proactively suggest items to list. For example, if the user is stressed, the data collection unit may refrain from suggesting items to list. For example, if the user is excited, the data collection unit may quickly suggest items to list. This allows for more appropriate listing timing by suggesting items according to the user's emotions.

[0103] The estimation unit can analyze a user's past listing history and estimate the optimal selling price. For example, the estimation unit can refer to the user's past successful selling prices. It can also avoid selling prices that the user has previously failed at. Furthermore, it can suggest the most effective selling price based on the user's past listing history. In this way, the optimal selling price can be estimated by analyzing past listing history.

[0104] The estimation unit can estimate the user's emotions and adjust the estimated selling price based on those emotions. For example, if the user is relaxed, the estimation unit will provide a detailed estimate. If the user is in a hurry, the estimation unit can provide a simplified estimate. If the user is excited, the estimation unit can provide a quick estimate. By adjusting the estimate according to the user's emotions, a more appropriate selling price can be presented.

[0105] The calculation unit can analyze the user's past income data to improve the accuracy of income calculations. For example, the calculation unit can estimate current income based on past income data. The calculation unit can also take into account seasonal fluctuations in income from past income data. For example, the calculation unit can analyze past income data to calculate the most appropriate income. This allows for improved calculation accuracy by referencing past income data.

[0106] The calculation unit can estimate the user's emotions and adjust the income calculation result based on the estimated emotions. For example, if the user is relaxed, the calculation unit will present a detailed calculation result. For example, if the user is in a hurry, the calculation unit can also present a simplified calculation result. For example, if the user is excited, the calculation unit can also present the calculation result quickly. In this way, by adjusting the calculation result according to the user's emotions, a more appropriate income amount can be presented.

[0107] The generation unit can analyze the user's past listing information and improve the accuracy of listing information generation. For example, the generation unit generates current listing information based on past listing information. For example, the generation unit can also generate the most effective listing information from past listing information. For example, the generation unit can analyze past listing information and suggest the optimal listing information. In this way, the accuracy of generation can be improved by referring to past listing information.

[0108] The generation unit can estimate the user's emotions and adjust the generated listing information based on those emotions. For example, if the user is relaxed, the generation unit will present detailed results. If the user is in a hurry, for example, the generation unit can present simplified results. If the user is excited, for example, the generation unit can present results quickly. By adjusting the generated results according to the user's emotions, more appropriate listing information can be presented.

[0109] The listing department can analyze users' past listing procedure data to improve the accuracy of the procedure. For example, the listing department can optimize the current procedure based on past listing procedure data. For example, the listing department can select the most effective procedure from past listing procedure data. For example, the listing department can analyze past listing procedure data and propose the optimal procedure. In this way, the accuracy of the procedure can be improved by referring to past listing procedure data.

[0110] The listing system can estimate the user's emotions and adjust the listing procedure results based on those emotions. For example, if the user is relaxed, the system may present detailed results. If the user is in a hurry, it may present simplified results. If the user is excited, it may present results quickly. By adjusting the results according to the user's emotions, the system can provide more appropriate listing procedures.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects information about products that users want to list for sale. For example, it can save product information entered by the user to a database, collect product images taken with a smartphone camera, and collect detailed product information (product name, category, condition, etc.). Step 2: The estimation unit analyzes the sales status of each flea market and auction site based on the information collected by the collection unit and estimates the selling price. For example, it can estimate the current market price based on past sales data, calculate the average selling price of a product by analyzing the sales history of each site, or estimate the selling price by considering the balance of supply and demand for the product. Step 3: The calculation unit calculates the revenue amount after deducting fees and shipping costs based on the sales price estimated by the estimation unit, and presents it to the user. For example, it can calculate the revenue amount considering the commission rates of each site and the shipping costs of the products, and provide an interface to present the revenue amount to the user. Step 4: The generation unit generates listing information such as titles, product descriptions, and categories that differ for each site, based on the revenue amount calculated by the calculation unit. For example, it can analyze product images to generate appropriate titles and detailed product descriptions, or automatically select product categories. Step 5: The listing unit automatically performs the listing process in conjunction with each site based on the listing information generated by the generation unit. For example, it can input the necessary information into each site's listing form, display confirmation messages to complete the listing process, and notify the user of the progress of the listing process.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, estimation unit, calculation unit, generation unit, and listing unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects product information from the user using the camera 42 and reception device 38 of the smart device 14. The estimation unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and analyzes the sales status of each flea market and auction site to estimate the selling price. The calculation unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and calculates the amount of revenue after deducting fees and shipping costs. The generation unit is implemented in the control unit 46A of the smart device 14, for example, and generates listing information from images. The listing unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and automatically performs the listing procedure in cooperation with each site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, estimation unit, calculation unit, generation unit, and listing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects product information from the user using the camera 42 and microphone 238 of the smart glasses 214. The estimation unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the sales status of each flea market and auction site and estimates the selling price. The calculation unit is implemented in the specific processing unit 290 of the data processing unit 12, which calculates the amount of revenue after deducting fees and shipping costs. The generation unit is implemented in the specific processing unit 46A of the smart glasses 214, which generates listing information from images. The listing unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically performs the listing procedure in cooperation with each site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, estimation unit, calculation unit, generation unit, and listing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects product information from the user using the camera 42 and microphone 238 of the headset terminal 314. The estimation unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the sales status of each flea market and auction site and estimates the selling price. The calculation unit is implemented in the specific processing unit 290 of the data processing unit 12, which calculates the amount of revenue after deducting fees and shipping costs. The generation unit is implemented in the specific processing unit 46A of the headset terminal 314, which generates listing information from images. The listing unit is implemented in the specific processing unit 290 of the data processing unit 12, which automatically performs the listing procedure in cooperation with each site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, estimation unit, calculation unit, generation unit, and listing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects product information from users using the camera 42 and microphone 238 of the robot 414. The estimation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the sales status of each flea market and auction site and estimates the selling price. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which calculates the amount of revenue after deducting fees and shipping costs. The generation unit is implemented by, for example, the control unit 46A of the robot 414, which generates listing information from images. The listing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically performs the listing procedure in cooperation with each site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A collection unit that collects information on products that users want to list for sale, Based on the information collected by the aforementioned collection unit, an estimation unit analyzes the sales status of each flea market and auction site and estimates the selling price. Based on the sales price estimated by the estimation unit, a calculation unit calculates the revenue amount after deducting fees and shipping costs and presents it to the user. Based on the revenue amount calculated by the calculation unit, a generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from the image, The system includes a listing unit that automatically performs listing procedures in cooperation with each site based on the listing information generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of product information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past listing history and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting product information, filtering is performed based on the user's current areas of interest and trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of products to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting product information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting product information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The estimation unit, We estimate user sentiment and adjust the pricing method based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The estimation unit, When estimating sales prices, we improve the accuracy of the estimation by referring to past sales data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The estimation unit, When estimating selling prices, different estimation algorithms are applied for each product category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The estimation unit, It estimates the user's emotions and adjusts how the estimation results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The estimation unit, When estimating the selling price, we prioritize the estimation based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The estimation unit, When estimating selling prices, we improve the accuracy of the estimates by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, The system estimates user sentiment and adjusts the revenue calculation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, When calculating revenue, we improve the accuracy of the calculation by referring to past fee and shipping cost data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, When calculating revenue, different calculation algorithms are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, It estimates the user's emotions and adjusts how the calculation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, When calculating income, the priority of calculation is determined based on the date the products were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit, When calculating revenue, we improve the accuracy of the calculation by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate the user's emotions and adjust the method of generating listing information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating listing information, we refer to product image analysis data to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating listing information, different generation algorithms are applied for each product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts how the generated results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating listing information, the generation priority is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating listing information, we refer to relevant market data for the product to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned exhibit section is, We estimate the user's sentiment and adjust the listing process based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned exhibit section is, During the listing process, we refer to past listing data to improve the accuracy of the process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned exhibit section is, When listing an item, a different procedural algorithm is applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned exhibit section is, The system estimates user sentiment and prioritizes listing procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned exhibit section is, When listing an item, the priority of the process is determined based on when the item was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned exhibit section is, During the listing process, we refer to relevant market data for the product to improve the accuracy of the process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information on products that users want to list for sale, Based on the information collected by the aforementioned collection unit, an estimation unit analyzes the sales status of each flea market and auction site and estimates the selling price. Based on the sales price estimated by the estimation unit, a calculation unit calculates the revenue amount after deducting fees and shipping costs and presents it to the user. Based on the revenue amount calculated by the calculation unit, a generation unit generates listing information such as titles, product descriptions, and categories that differ for each site from the image, The system includes a listing unit that automatically performs listing procedures in cooperation with each site based on the listing information generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of product information collection based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past listing history and select the most suitable information gathering method. The system according to feature 1.

4. The aforementioned collection unit is When collecting product information, filtering is performed based on the user's current areas of interest and trends. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of products to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting product information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting product information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

8. The estimation unit, We estimate user sentiment and adjust the pricing method based on that estimated sentiment. The system according to feature 1.

9. The estimation unit, When estimating sales prices, we improve the accuracy of the estimation by referring to past sales data. The system according to feature 1.

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