Automated equipment and control method for cross-border e-commerce

Abstract

This invention provides an automated device and a control method thereof that automates a series of processes that a seller must perform when acting as an agent for overseas purchases. [Solution] The automated product sourcing device includes a database for storing data and a processor for controlling the automated device. When the processor finds pre-set words indicating popular search keywords while scraping web pages, it extracts keywords that have been searched more than a pre-set frequency and stores them in the database. It then uses the extracted and stored keywords to search from sales platforms, stores the URL addresses of online shops found on the sales platforms in the database, and uses the stored URL addresses of online shops to store product data from those online shops in the database.

Description

Technical Field

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[0001] The present invention relates to an automation device for cross-border EC and a control method thereof, and more particularly, to an automation device for cross-border EC that automates a series of processes that should be performed in the process of a seller acting as an overseas purchasing agent, and a control method thereof.

Background Art

[0002] Cross-border EC that acts as an overseas purchasing agent in the e-commerce market generally goes through the processes of product sourcing, product processing, product registration, order collection, product delivery, and CS. That is, the seller searches for overseas products, translates their content or changes the images, and registers them on the open market. The registered products are exposed to purchasers (consumers), and when a purchaser purchases, the order is received by the seller. The seller purchases the purchaser's goods on their behalf and delivers the goods to the purchaser's address. Also, the seller should respond to the purchaser's inquiries throughout all these processes.

[0003] However, there is no solution that automates each of these processes, and there is a problem that the seller has to solve all the difficulties by themselves. For example, there are difficulties in searching for popular products in the process of sourcing products, in creating a new introduction page so that domestic customers can understand the products even if they are found, in considering many factors such as overseas delivery fees when setting the price of the products to be sold, in not being able to deliver domestically immediately when an order is received, and in responding to the purchaser's inquiries.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention is for solving the above-described problems, and an object thereof is to provide an automation device that can automate and provide a series of processes that should be performed in the process of a seller acting as an overseas purchasing agent, and a control method thereof.

Means for Solving the Problems

[0005] An automated device for cross-border e-commerce according to one embodiment of the present invention for achieving the above objective includes a database for storing data and a processor for controlling the automated device, wherein the processor, when it finds pre-set words indicating popular search keywords while scraping web pages, extracts keywords that have been searched more than a pre-set frequency and stores them in the database, searches from sales platforms using the extracted and stored keywords, stores the URL addresses of online shops found from the sales platforms in the database, and stores product data of the online shops in the database using the stored URL addresses of the online shops.

[0006] Furthermore, when the processor identifies a pre-set word indicating a popular search keyword, it can analyze the scrapped web page to find the associated search engine, extract keywords that have been searched more than a pre-set frequency through the found search engine, and store them in the database.

[0007] Furthermore, the processor may use the URL address of the online shop that has been stored to save the product data of the online shop as first temporary data in the database, compare the stored first temporary data with the infringement judgment data, and if the first temporary data corresponds to the infringement judgment data as a result of the comparison, it may determine that the data cannot be sold and delete it from the database.

[0008] Furthermore, the infringement determination data includes prohibited word data that has been set in advance based on the deleted data and trademarked words, and the processor can control the database to add the first temporary data, which is determined to be unsaleable data, to the deleted data.

[0009] Furthermore, the processor may use the product images constituting the first temporary data to search for similar products and control the database to include the data relating to the searched similar products in the first temporary data and save it as final product data.

[0010] The processor can then use the stored final product data to generate option data to be attached to each of the multiple products found from the first online shop and at least one other online shop, and use the generated option data to generate an option page that configures the multiple products found as different options for a single product.

[0011] Furthermore, the processor may compare the first temporary data of the stored final product data with the data of similar products that have been searched, extract the differences between multiple products searched from the first online shop and at least one other online shop, link the extracted differences to a pre-set option classification criterion, and generate option data based on the linked option classification criterion.

[0012] The processor can then generate an option page with a function to select products corresponding to each option based on the generated option data, set an option price for each product corresponding to each option based on the price at which the products are sold in the online shop that sources the products, shipping costs by product category, exchange rates, margins, payment processing fees, and shipping agent information, and determine the list price and selling price to be displayed on the option page based on the set option prices.

[0013] Furthermore, the processor may select one of several trained translation models based on the category of the target product for which the option page was generated, translate the option page using the selected trained translation model, and combine the translated option page with a product page containing a header content file, a footer content file, and the saved final product data to generate and save a product detail page.

[0014] The processor may identify images or text related to prices present on product pages included in the stored final product data, and may remove the identified images or text. On the other hand, a control method for an automated device according to one embodiment of the present invention for achieving the above objective may include the steps of: scraping a web page; if a pre-set word indicating a popular search keyword is found during the scrapping process, extracting and saving the keyword that has been searched more than a pre-set frequency; searching from a sales platform using the extracted and saved keyword; saving the URL address of the online shop found from the sales platform; and saving the product data of the online shop using the saved URL address of the online shop.

[0015] The step of extracting and saving the searched keywords may include, once a pre-set word indicating the popular search keywords is identified, the step of analyzing the scrapped web page to find the associated search engine, and the step of extracting and saving keywords that have been searched more than a pre-set frequency through the found search engine.

[0016] Furthermore, the step of saving the product data of the online shop may include the steps of saving the product data of the online shop as first temporary data using the URL address of the online shop that has been saved; comparing the saved first temporary data with the infringement judgment data; and, as a result of the comparison, determining that the first temporary data corresponds to the infringement judgment data and deleting it if it is determined that the data cannot be sold.

[0017] Furthermore, the infringement determination data includes prohibited word data that has been set in advance based on the deleted data and trademarked words, and the deletion step may further include the step of adding the first temporary data that has been determined to be unsaleable to the deleted data.

[0018] Furthermore, the step of saving the product data of the online shop may further include the steps of searching for similar products using the product images that constitute the first temporary data, and saving the first temporary data with data relating to the searched similar products as final product data.

[0019] The method may further include the steps of: generating option data to be attached to each of the multiple products searched from the first online shop and at least one other online shop using the saved final product data; and generating an option page that configures the multiple products searched as different options for a single product using the generated option data.

[0020] Furthermore, the step of generating the option data may include: comparing the first temporary data of the stored final product data with the data of similar products that have been searched, extracting differences between multiple products searched from the first online shop and at least one other online shop; linking the extracted differences to pre-set option classification criteria; and generating option data based on the linked option classification criteria.

[0021] The step of generating the options page may include: generating an options page with a function that allows the user to select a product corresponding to each option based on the generated option data; setting an option price for each product corresponding to each option based on the price at which the product is sold in the online shop that sources the product, shipping costs by product category, exchange rates, margins, payment processing fees, and shipping agent information; and determining the list price and selling price to be displayed on the options page based on the set option prices. Alternatively, the process may further include the steps of: selecting one of several trained translation models based on the category of the target product from which the option page was generated; translating the option page based on the selected trained translation model; and generating and saving a product detail page by combining the translated option page with a product page containing a header content file, a footer content file, and the saved final product data.

[0022] The step of generating and saving the product details page may include the steps of identifying an image or text relating to the price present on the product page included in the saved final product data, and removing the identified image or text. [Effects of the Invention]

[0023] According to various embodiments of the present invention as described above, it is possible to source for a seller who attempts to source products from an overseas sales platform and sell them in the domestic open market products that are likely to sell well and products that will not cause problems even if sold. Also, it is possible to generate a page containing product information that can be automatically placed on the open market for a seller who attempts to source products from an overseas sales platform and sell them in the domestic open market. And, there is an effect that products to be automatically sold on the open market can be registered, and new products can be changed and registered at regular intervals, and orders collected from multiple open markets can be automatically processed, and cases where damage occurs to the seller can be filtered in advance.

Brief Description of the Drawings

[0024] [Figure 1] It is a conceptual diagram for explaining the operation of an automation device for cross-border EC according to an embodiment of the present invention. [Figure 2] It is a schematic block diagram for explaining the configuration of an automation device according to an embodiment of the present invention. [Figure 3] It is a conceptual diagram for explaining the operation of a sourcing module according to an embodiment of the present invention. [Figure 4] It is a conceptual diagram for explaining the operation of a processing module according to an embodiment of the present invention. [Figure 5] It is a conceptual diagram for explaining the operation of a registration module according to an embodiment of the present invention. [Figure 6] It is a conceptual diagram for explaining the operation of an order processing module according to an embodiment of the present invention. [Figure 7] It is a conceptual diagram for explaining the operation of a purchase module according to an embodiment of the present invention. [Figure 8] It is a conceptual diagram for explaining the operation of a delivery module according to an embodiment of the present invention. [Figure 9] It is a conceptual diagram for explaining the operation of a CS module according to an embodiment of the present invention. [Figure 10]This is a flowchart illustrating a control method for an automated device according to one embodiment of the present invention. [Figure 11] This is a flowchart illustrating a control method for an automated device according to one embodiment of the present invention. [Figure 12] This is a flowchart illustrating a control method for an automated device according to one embodiment of the present invention. [Figure 13] This is a flowchart illustrating a control method for an automated device according to one embodiment of the present invention. [Modes for carrying out the invention]

[0025] The following describes various embodiments of this document with reference to the accompanying drawings. However, this should be understood not as limiting the technology described in this document to specific embodiments, but rather as including various modifications, equivalents, and / or alternatives to the embodiments described in this document. In describing the drawings, similar reference numerals may be used for similar configurations. In this document, expressions such as "possess," "may possess," "include," or "may include" refer to the existence of the relevant feature (e.g., numerical values, functions, operations, or components such as parts), and do not exclude the existence of additional features.

[0026] In this document, expressions such as "A or B," "A and / or B at least one," or "A and / or B one or more" may include all possible combinations of the items listed together. For example, "A or B," "A and B at least one," or "A or B at least one" may refer to (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B. Expressions such as "first," "second," "initial," or "second" used in this document may modify a variety of components regardless of their order and / or importance, and are used only to distinguish one component from others, without limiting the component in question.

[0027] The expression "configured to" used in this document can be replaced with other expressions depending on the context, such as "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" does not necessarily mean "specifically designed to" in terms of hardware. Instead, in some situations, the expression "a device configured to" may mean that the device "can" do something together with other devices or other components.

[0028] The terms used herein are for illustrative purposes only and are not intended to limit and / or restrict the use of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "includes" or "has" specify the presence of features, figures, actions, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the presence or possibility of adding one or more other features, figures, actions, components, parts, or combinations thereof.

[0029] In the embodiments, a "module" or "part" performs at least one function or operation and can be embodied as hardware or software, or as a combination of hardware and software. Furthermore, multiple "modules" or multiple "parts" can be integrated into at least one module and embodied by at least one processor, except for "modules" or "parts" that need to be embodied by specific hardware.

[0030] The present invention will be described in detail below with reference to the attached drawings. Figure 1 is a conceptual diagram illustrating the operation of an automated device 1000 for cross-border e-commerce according to one embodiment of the present invention. The automated device 1000 may operate in a cloud environment as a SaaS, or it may operate in an on-premise configuration. In the example in Figure 1, the functions and operation of the automated device 1000 are explained in a total of seven modules for ease of understanding. Furthermore, the explanation will focus on the automated device 1000 operating in a SaaS-type cloud.

[0031] Referring to Figure 1, the automated device 1000 can perform overseas purchasing agency operations by sending and receiving data with servers 2000 of domestic and international open markets, servers 3000 of shipping agents, and servers 4000 of online shops. The automated device 1000 also visualizes data related to products, purchases, payments, delivery, and customer inquiries, making it available for viewing on the seller terminal 5000 and / or administrator terminal 6000. Referring to Figure 1, the terms used in this specification are summarized below. A seller is a person who acts as an agent for a buyer to purchase goods through overseas purchasing agency services. A seller is a user of the automated device 1000, who has obtained usage rights through a subscription or similar arrangement. A buyer means a general consumer. An open market is a platform where sellers sell goods to buyers. An online shop is a platform where sellers source items that buyers request to purchase. While open markets and online shops can be the same platform, online shops are used to mean where sellers source goods, while open markets are used to mean where sellers sell the goods they have sourced.

[0032] The functions and operations of the automated device 1000 can be broadly divided into seven categories, which are named the sourcing module 1100, processing module 1200, registration module 1300, order processing module 1400, purchase module 1500, delivery module 1600, and CS module 1700.

[0033] Figure 2 is a schematic block diagram illustrating the configuration of an automated device 1000 according to one embodiment of the present invention. Referring to Figure 2, the automated device 1000 may include a database 10, a communication unit 20, and a processor 30. Of course, it may also include other components such as an input unit (not shown), a display (not shown), and memory (not shown).

[0034] Database 10 can store data necessary to drive the automated device 1000. Furthermore, database 10 can perform operations such as moving, copying, and deleting previously stored data under the control of the processor 30. For example, database 10 may contain a variety of data, including keyword data, online shop-related data, product data, infringement judgment data, data on similar products, option data, order data, inventory data, and delivery data.

[0035] Memory (not shown) can be used as a concept that includes database 10. Memory (not shown) can store various modules, software, functions, AI trained models, data, etc., for driving the automated device 1000. For example, memory (not shown) may store a function that performs an action to extract product data, and may store at least one AI trained model for translating each product category into a suitable format.

[0036] The communication unit 20 communicates with external servers and devices such as the open market server 2000, the shipping agent server 3000, the online shop server 4000, the seller terminal 5000, and the administrator terminal 6000. For example, the automated device 1000 may connect to a web page on an external server via the communication unit 20 in order to collect product data.

[0037] To this end, the communication unit 20 can utilize wired communication methods such as LAN and HDMI (registered trademark), and various wireless communication methods such as wireless LAN, NFC, IR communication, Zigbee communication, and Bluetooth (registered trademark).

[0038] The processor 30 can control the rest of the configuration of the automated device 1000. For example, the processor 30 can control the database 10 to include data on similar products in the first temporary data and save it as final product data.

[0039] The processor 30 may be implemented as a single CPU and perform tasks such as product sourcing, product information processing, product registration, and order processing, or it may be implemented as multiple processors and IP that performs specific functions. The specific operation of the processor 30 will be described later.

[0040] Figure 3 is a conceptual diagram illustrating the operation of a sourcing module 1100 according to one embodiment of the present invention. Referring to Figure 3, the sourcing module 1100 may include a popular search keyword extraction function 1110, a product data extraction function 1120, a product filter function 1130, and a similar product extraction function 1140. Since these modules and functions are read and operated via the processor 30, for the sake of explanation, the names of the processor 30 and each module and function will be used interchangeably in the following explanation. The processor 30 can call the popular search keyword extraction function 1110 to extract and save keywords that have been searched more than a preset frequency. This function finds keywords that match trends in order to source products that are likely to sell in large numbers. The processor 30 can also scrap web pages. If the processor 30 finds a preset word indicating a popular search keyword during the scrapping process, it can extract the keyword and save it to the database 10. For example, if a word like "Top 10 Popular Search Keywords" is found on a web page being scrapped, the processor 30 can be triggered to extract and save popular keywords.

[0041] More specifically, when the processor 30 identifies pre-set words indicating popular search keywords, such as "Top 10 Popular Search Keywords," it can analyze the scrapped web pages to find the associated search engines. Then, the processor 30 can extract keywords that have been searched more than a pre-set frequency through the search engines found during the scrapping process and store them in the database 10. For example, the processor 30 can extract popular keywords using the search engine's trend analysis function.

[0042] Next, the processor 30 can call the product data extraction function 1120 to collect information about online shops selling products corresponding to the extracted keywords. The processor 30 can use the keywords extracted and stored in the database 10 to search from sales platforms. Sales platforms can be various overseas open marketplaces or the company's own online shop. The processor 30 can scrap the URL addresses and names of the online shops found from the sales platforms and store them in the database 10. In the following, actions such as receiving data from online shops can be interpreted as receiving data from the online shop's server 4000. For the sake of explanation, the terms online shop and online shop's server 4000 will be used interchangeably.

[0043] The processor 30 then calls the product filter function 1130 to collect product data and may delete product data for products that may infringe intellectual property rights and therefore cannot be sold. The processor 30 may use the collected URL addresses of online shops to store the online shop's product data in the database 10.

[0044] More specifically, the processor 30 can use the saved URL address of the online shop to scrap the product data of the online shop. In this case, the processor 30 can save the product data of the online shop as first temporary data. Then, the processor 30 can compare the saved first temporary data with the infringement judgment data.

[0045] Infringement judgment data may include prohibited word data and deletion data. Prohibited word data may be a pre-configured dataset based on trademarked words. Deletion data may be a dataset containing names, trademarks, and designations of products that have been deleted as determined to be unsaleable by the operation of the product filter function 1130.

[0046] If, as a result of comparison with infringement judgment data, the first temporary data is found to be broken in correspondence with the infringement judgment data, the processor 30 may determine that the first temporary data pertains to a product that cannot be sold and delete it. The processor 30 may then add the first temporary data that it determined to be unsellable as deleted data and manage it accordingly. This allows for the continuous updating of the dataset used to determine the likelihood of intellectual property infringement.

[0047] The processor 30 may call the similar product extraction module 1140 and use the stored product data to search for similar products and manage them together. The processor 30 may extract images of products that constitute the first temporary data. Then, the processor 30 may use the extracted product images to search for similar products. Next, the processor 30 may control the database 10 to include data about the searched similar products in the first temporary data and save it as final product data. In addition, when the processor 30 saves the URL address of an online shop, it may use the name of the online shop, which is also saved, to prevent duplicate data collection when searching for similar product data.

[0048] Figure 4 is a conceptual diagram illustrating the operation of a processing module 1200 according to one embodiment of the present invention. Referring to Figure 4, the processing module 1200 may include an option page generation function 1210, an option translation function 1220, and a detail page creation function 1230. Since these modules and functions are read and operated via the processor 30, for the sake of convenience in the following explanation, the names of the processor 30 and each module and function will be used interchangeably in some cases.

[0049] The processor 30 calls the option page generation function 1210 to create various options for a single product, specifically by combining information about the product to be sold, particularly multiple similar products sourced from various online shops. More specifically, the processor 30 can retrieve data about similar products stored by the sourcing module 1100 and classify them as options. The processor 30 can control the database 10 to store product data from the first online shop from which the product is sourced as first temporary data. The processor 30 can then use the product images that make up the first temporary data to search for similar products from at least one online shop different from the first online shop. The data about the searched similar products can be stored together with the first temporary data as final product data.

[0050] The processor 30 can use the saved final product data to generate option data to be attached to each of the multiple products searched from the first online shop and at least one other online shop. The processor 30 can then use the generated option data to generate an option page that configures the searched "multiple products" as "different options for a single product".

[0051] More specifically, when generating option data, the processor 30 may compare the first temporary data of the stored final product data with data on similar products. The processor 30 may then extract the differences between these multiple products. The processor 30 may then link the extracted differences to pre-defined option classification criteria. By linking them to option classification criteria with certain standards, the generation of option pages can be made consistent. For example, option classification criteria may consist of standardized specifications such as hue, size, dimensions, and length. The processor 30 may then generate option data based on the linked option classification criteria. Next, the processor 30 may generate an option page with the functionality to select a product corresponding to each option based on the generated option data. In some cases, the number of option data may exceed one; in such cases, the option page is generated based on the first option, and subsequent options may be displayed in combination. When multiple option data exists, which option is set as the first option may be determined by a predetermined priority order for option selection, which is determined by the product category. For example, depending on the product category, the customer's selection criteria may be hue or size.

[0052] When processor 30 generates the options page, it may set different prices for each product corresponding to each option. Since different options are likely to be sourced from different online shops, their prices may not be consistent. Setting option prices is intended to adjust for such price inconsistencies.

[0053] In more detail, when setting option prices, processor 30 may set option prices for each corresponding product based on the price at which the corresponding product is sold on the online shop that sources the product, shipping costs by product category, exchange rates, margins, payment processing fees, and information on the shipping agent.

[0054] The processor 30 can then determine the list price and selling price to be displayed on the option page based on the set option price. For example, the list price may be set to 100% of the option price. The selling price is set to be lower than or the same as the option price, but the processor 30 may set the selling price to a value between 100% and 50% of the option price by deriving items that can be priced lower based on the data of each item used to set the option price.

[0055] Processor 30 can call the option translation function 1220 to perform translations appropriate for each product category. Specifically, processor 30 can select one of several pre-trained translation models based on the category of the product for which the option page was generated. These pre-trained translation models may be AI models that have learned universally used language, terminology, nuances, etc., in the relevant field based on the category. Processor 30 can translate the option page using the selected pre-trained translation model.

[0056] Next, the processor 30 may call the detail page creation function 1230 to generate a detail page for a product to be registered with the open market server 2000. Specifically, the processor 30 may generate a detail page for a product by combining a translated options page, a header content (images, audio, video, etc.) file, a footer content (images, audio, actions, etc.) file, and a product page containing the saved final product data. The processor 30 may then control the database 10 to save the generated detail page for the product.

[0057] When generating a product detail page, the processor 30 can identify images or text related to pricing present on the product page in the stored final product data. For example, it can identify images where a portion of the image contains the local selling price in another country. The processor 30 can then remove the identified image or text. Of course, it can perform image processing so that the removed portion does not appear unnatural. This prevents the product detail page from containing pricing information different from the selling price set via option pricing.

[0058] Figure 5 is a conceptual diagram illustrating the operation of a registration module 1300 according to one embodiment of the present invention. Referring to Figure 5, the registration module 1300 may include a product registration function 1310 and a product initialization function 1320. Since these modules and functions are read and operated via the processor 30, for the sake of explanation, the names of the processor 30 and each module and function will be used interchangeably in the following explanation. In the following explanation, operations such as receiving data from the open market may be interpreted as receiving data from the open market server 2000. For the sake of explanation, the open market and the open market server 2000 will be used interchangeably.

[0059] The processor 30 can call the product registration function 1310 to register products in various open marketplaces. In order to register in each open marketplace, data for product registration should be generated according to the requirements of each open marketplace. To this end, the processor 30 can generate a data format according to the respective requirements for registration in at least one open marketplace. The processor 30 can then convert product data, including the product detail page that has been stored in advance, into JSON format, and apply the converted product data to the generated data format to generate data that can be used to proceed with product registration.

[0060] The processor 30 can then use the generated product registration-ready data to register the product on behalf of the seller in the corresponding open market. In this case, seller authentication is required to register the product. To this end, once the product registration-ready data is generated, the processor 30 can control the communication unit 20 to transmit authentication for product registration to the seller's terminal 5000. Once the key value is received from the seller's terminal 5000 via the communication unit 20, the processor 30 can compare the received key value with the key value stored in the database 10 to complete the authentication. After authentication is complete, the processor 30 can use the product registration-ready data to register the product in the open market.

[0061] Then, when the processor 30 receives confirmation from the open market server 2000 via the communication unit 20 that product registration has been completed, it can link the registered product with the seller and save it in the database 10. By linking the seller and the product, it is possible to request payment from the seller when an order for the relevant product is placed, notify the seller when a customer service (CS) occurs, and settle the sales revenue with the seller when it is generated.

[0062] Processor 30 can call the product initialization function 1320 to initialize product registration and register new products. Open marketplaces are platforms where products reflecting trends sell well, so it is not appropriate to register and sell the same product for a long period of time without any changes. This is especially true for sellers who send revenue through overseas purchasing agents. For this reason, after product registration is complete, processor 30 may initialize product registration after a predetermined period has elapsed. For example, initialization may be performed one month after product registration, or on the first of every month.

[0063] More specifically, processor 30 can delete all registered products from the open market. It can then classify the products stored in database 10 in order to select new products to register. Even after product registration, products will continue to be added to database 10 by the sourcing module 1100 and processing module 1200. Processor 30 can classify the products stored in database 10 into products that have been sold, products that have not been sold, and newly sourced products. Products that have been sold can be classified based on whether or not there is a breakdown of sales. Products that have not been sold and newly sourced products can be classified based on the date they were registered in database 10.

[0064] Next, the processor 30 can adjust the proportion of each category of goods according to the distribution model. For example, the distribution model may be a trained model that takes at least one of the following as input values: the frequency of sales, the set sales price, and the subscription period of the seller, and outputs the proportion of goods distribution that maximizes total sales. Once the optimal proportion of goods distribution is determined, the processor 30 can re-register the goods with the adjusted proportions. The process of re-registering the goods is the same as the process of registering the goods in the open market by calling the goods registration function 1310 described above.

[0065] Figure 6 is a conceptual diagram illustrating the operation of an order processing module 1400 according to one embodiment of the present invention. Referring to Figure 6, the order processing module 1400 may include an inventory confirmation function 1410, a margin calculation function 1420, a personal customs clearance unique code confirmation function 1430, and an order approval function 1440. Since these modules and functions are read and operated via the processor 30, for the sake of explanation, the names of the processor 30 and each module and function will be used interchangeably in the following explanation.

[0066] The processor 30 can collect and process orders from various open marketplaces operated by the order processing module 1400 in a single batch. In particular, it collects inventory levels and fluctuating price information from online shops that source products, and cancels orders if they do not meet pre-set conditions, thereby preventing situations where sellers would suffer losses.

[0067] When the processor 30 receives an order signal via the communication unit 20 from at least one open market server 2000 where the product is registered, it can call the inventory check function 1410 to determine whether the inventory quantity of the product corresponding to the received order signal is equal to or greater than the order quantity. More specifically, the processor 30 can send a signal to the online shop server 4000 that sources the product to check the inventory quantity of the product, and then determine whether the order can be processed by comparing the inventory quantity information received from the online shop server 4000 with the order quantity.

[0068] If the inventory quantity of a product is determined to be greater than or equal to the order quantity, the processor 30 calls the margin calculation function 1420 to calculate the margin. In this case, the margin may be expressed as a monetary amount or as a margin rate expressed as a percentage. The processor 30 may control the communication unit 20 to receive price information of the products sold by the online shop that has confirmed the inventory quantity of the product. The processor 30 may then calculate the margin by comparing the received price information of the product with the sales price registered to the open market server 2000. Since the price may have been changed at the online shop that sources the product, a new margin is calculated and compared with the margin that was set when the previous sales price was determined. When determining the sales price, the processor 30 may determine that the condition is satisfied if the current margin is higher than or equal to the set margin. Conversely, when determining the sales price, if the margin set is lower than the current margin, the processor 30 may determine whether it falls within the acceptable margin range and determine whether the condition is satisfied.

[0069] If the calculated margin does not satisfy the pre-set conditions, the processor 30 may control the communication unit 20 to send a signal to the open market server 2000 from which the order signal was received to cancel the order corresponding to the received order signal. Conversely, if the calculated margin satisfies the pre-set conditions, the processor 30 may control the database 10 to change the state of the received order signal to an orderable state.

[0070] Next, the processor 30 may call the personal customs clearance unique code verification function 1430 to check whether the information necessary for overseas purchasing has been entered. Once it is confirmed that the status stored in the database 10 has been changed to the orderable status, the processor 30 may determine whether a value corresponding to the personal customs clearance unique code exists. If it is determined that a value corresponding to the personal customs clearance unique code has been entered in advance and exists, the processor 30 may control the database 10 to change the orderable status to the shipping ready status. Conversely, if there is no value corresponding to the personal customs clearance unique code, the processor 30 may wait until a value is entered.

[0071] Once it is confirmed that the status has changed to "ready for delivery," the processor 30 may call the order product function 1400 to approve the order. Then, once the order is approved, the processor 30 may control the communication unit 20 to transmit information about the approved products to the seller terminal 5000. The automated system does not require the seller to directly approve the order, but it informs the seller of the details of the approved order.

[0072] Figure 7 is a conceptual diagram illustrating the operation of a purchase module 1500 according to one embodiment of the present invention. Referring to Figure 7, the purchase module 1500 may include a settlement request function 1510, a settlement approval function 1520, an order placement function 1530, and an order breakdown transmission function 1540. Since these modules and functions are read and operated via the processor 30, for the sake of convenience in the following explanation, the names of the processor 30 and each module and function will be used interchangeably in some cases.

[0073] The processor 30 can automate the process of batch processing purchases from various online shops based on order breakdowns from multiple open market servers 2000 via the purchase module 1500. The processor 30 can control the database 10 to change its status from "ready for delivery" to "order approved" once an order is approved. Once it is confirmed that the status has changed to "order approved," the processor 30 can control the communication unit 20 to call the payment request function 1510 to approve a payment request message for the breakdown to be paid by the seller to the seller terminal 5000. The processor 30 can then control the database 10 to change its status back to "payment request."

[0074] Next, the processor 30 calls the settlement approval function 1520 to perform the settlement. When a settlement progress request signal is received from the seller terminal 5000, the processor 30 may control the communication unit 20 to retrieve the settlement details from the database 10 to configure the screen and send the configured screen to the seller terminal 5000. When a signal indicating that the settlement has been approved is received from the seller terminal 5000, the processor 30 may control the database 10 to change its state to the settlement approval state.

[0075] Once it is confirmed that the status has been changed to payment approval, the processor 30 may call the order placement function 1530 to place an order for the goods. The processor 30 may retrieve the list of goods in the payment approval status from the database 10, create an order form in a format suitable for each online shop's server 4000, and transmit it. The processor 30 may also use the payment methods provided by each online shop's server 4000 to perform the payment. The processor 30 may control the database 10 to change the status to "order placed" for goods for which payment has been completed at the online shop.

[0076] Next, the processor 30 may control the communication unit 20 to call the order breakdown transmission function 1540 and send the breakdown, including the completed payment for the online shop, to the shipping agent server 3000. The information sent to the shipping agent server 3000 may include the country code of the origin, the country code of the destination, the shipping method, the customs clearance category, the individual customs clearance code, the recipient information, the product information, the inspection option, whether the freight delivery fee can be paid, and whether the customs and surcharges can be paid. In response, the shipping agent server 3000 sends the tracking number for the destination country of the transmitted goods. The processor 30 may control the database 10 to store the tracking number for the destination country received via the communication unit 20.

[0077] Figure 8 is a conceptual diagram illustrating the operation of a delivery module 1600 according to one embodiment of the present invention. Referring to Figure 8, the delivery module 1600 may include a tracking number confirmation function 1610, a tracking number transmission function 1620, and a package tracking function 1630. Since these modules and functions are read and operated via the processor 30, for the sake of explanation, the names of the processor 30 and each module and function will be used interchangeably in the following explanation.

[0078] The processor 30 can call the tracking number confirmation function 1610 at pre-set time intervals to confirm the tracking number during delivery. The tracking number information can be received from the forwarding server 3000. The processor 30 can then control the database 10 to update the confirmed information. The processor 30 can configure a screen showing the delivery status and control the communication unit 20 to send the configured screen to the seller terminal 5000.

[0079] Next, the processor 30 calls the tracking number transmission function 1620 to transmit the tracking number of the destination country to the open market server 2000 where the purchase occurred, enabling domestic tracking of the package. While the package is being shipped overseas, it cannot be tracked by the open market server 2000, but once the package is delivered domestically, it becomes possible to track it via the open market server 2000.

[0080] The processor 30 calls the package tracking function 1630 to provide the seller terminal 5000 with the delivery status. The processor 30 can visualize the delivery status in the database 10 in real time. The processor 30 can then control the communication unit 20 to transmit the visualized information to the seller terminal 5000. Since package tracking is not possible via the open market server 2000 when the package is being shipped overseas, if the processor 30 receives a signal from the seller terminal 5000 requesting that information be provided to the buyer regarding the item being shipped overseas, the processor 30 can control the communication unit 20 to send an information message to the buyer regarding the current delivery status overseas.

[0081] Figure 9 is a conceptual diagram illustrating the operation of a CS module 1700 according to one embodiment of the present invention. Referring to Figure 9, the CS module 1700 may include a return processing function 1710, an exchange processing function 1720, a cancellation processing function 1730, an inquiry confirmation function 1740, and an inquiry processing function 1750. Since these modules and functions are read and operated via the processor 30, for convenience of explanation below, the names of the processor 30 and each module and function will be used interchangeably in some cases.

[0082] The processor 30 can verify the information sent to the seller via the open market server 2000 by the CS module 1700 and take action automatically.

[0083] The processor 30 may call the return processing function 1710 at pre-set intervals to request data on the products for which a return request has been made from the open market server 2000. Based on the received data, the processor 30 updates the database 10. Products for which a return request has been made can be visualized by the processor 30 and transmitted to the administrator terminal 6000. When a return approval signal is received from the administrator terminal 6000, the processor 30 may control the database 10 so that the information that the return has been approved is updated.

[0084] The processor 30 may call the exchange processing function 1720 at pre-set intervals to request data on the products for which an exchange request has occurred from the open market server 2000. Based on the received data, the processor 30 updates the database 10. Products for which an exchange request is pending may be visualized by the processor 30 and transmitted to the administrator terminal 6000. When an exchange approval signal is received from the administrator terminal 6000, the processor 30 may control the database 10 so that the information that the exchange has been approved is updated.

[0085] The processor 30 may call the cancellation processing function 1730 at pre-set intervals to request data on the product for which a cancellation request has been made from the open market server 2000. Based on the received data, the processor 30 processes the order cancellation through the following steps. First, the processor 30 determines whether the order has been canceled or not. If the order has not yet been canceled, the processor 30 may control the database 10 to immediately cancel the order and update the product data. Conversely, if the order has already been processed, the processor 30 rejects the cancellation request. The processor 30 may then control the communication unit 20 to send a message to the buyer informing them that cancellation is difficult because the purchase or delivery process is in progress, but that returns are possible in the future.

[0086] The processor 30 can call the inquiry confirmation function 1740 at pre-set intervals to collect information on what buyers have sent to the open market server 2000. The collected inquiry information is stored in the database 10, and the processor 30 can visualize the relevant information and control the communication unit 20 to transmit the visualized information to the seller terminal 5000 and the administrator terminal 6000.

[0087] The inquiry processing function 1750 is called by the processor 30 when an operation signal is received from the administrator terminal 6000 via the communication unit 20. The processor 30 uses a chatbot to explore the context of the inquiry made by the buyer and may classify it into simple or complex inquiries. If classified as a simple inquiry, the processor 30 may send a rule-based response to the open market server 2000. If classified as a complex inquiry, the processor 30 may send the response received from the administrator terminal 6000 to the open market server 2000.

[0088] According to the various embodiments of the present invention described above, sellers who sell goods on the open market through overseas purchasing agents can reduce the effort required to find goods, process goods, register goods, purchase goods when an order is placed, process deliveries, and manage customer service (CS), and of course, reduce the associated costs.

[0089] Furthermore, this cost-saving sales process allows buyers to receive inexpensive goods quickly and also ensures that defective products are dealt with promptly.

[0090] The following sections will provide further explanations using flowcharts to help understand the various embodiments of the present invention described above. The specific control methods for the automated device 1000 described in the flowcharts correspond to the contents of the automated device 1000 described above.

[0091] Figure 10 is a flowchart illustrating a control method for an automated device 1000 according to one embodiment of the present invention. In particular, Figure 10 illustrates a method for automating product sourcing. According to Figure 10, the automated device 1000 can scrap web pages S1010. If a pre-set word indicating a popular search keyword is identified during the scrapping process, the automated device 1000 can extract and save the keyword that has been searched more than a pre-set frequency S1020. More specifically, if a pre-set word indicating a popular search keyword is identified, the automated device 1000 can analyze the scrapped web page and find out what the associated search engine is. Then, it can extract and save the keyword that has been searched more than a pre-set frequency through the found search engine. For example, keywords that have been searched more than a pre-set frequency may be extracted using the trend analysis function of the search engine.

[0092] Next, the automated device 1000 can search the sales platform using the extracted and saved keywords S1030. Then, the automated device 1000 can save the URL address of the online shop found on the sales platform S1040. In this case, in addition to the URL address of the online shop, the name of the online shop may also be saved. For example, the name of the online shop can be used as a keyword to prevent duplicate searches when searching for similar products.

[0093] The automated device 1000 can save product data of an online shop using the URL address of the online shop that has been saved (S1050). More specifically, the automated device 1000 can save the product data of the online shop as first temporary data. Then, it can compare the first temporary data with infringement judgment data to determine whether it is data relating to a product that can be sold. If it corresponds to infringement judgment data, the first temporary data can be determined to be data that cannot be sold and deleted. The infringement judgment data is divided into prohibited word data and deleted data, and the first temporary data that has been deleted in correspondence with the infringement judgment data can be added to the deleted data. This can result in the effect of continuously updating the infringement judgment data.

[0094] Furthermore, the automated device 1000 can search for similar products using images of products that constitute the first temporary data. The data regarding the searched similar products can be included in the first temporary data and saved as final product data. Figure 11 is a flowchart illustrating a control method for an automated device 1000 according to one embodiment of the present invention. In particular, Figure 11 illustrates a method for automating the processing of product pages. Referring to Figure 11, the automated device 1000 can save product data from a first online shop that sources products as first temporary data S1110. Then, the automated device 1000 uses the product images constituting the first temporary data to search for similar products from at least one online shop different from the first online shop S1120, and can save the data related to the similar products found from the first temporary data as final product data S1130.

[0095] Next, the automated device 1000 can use the stored final product data to generate option data for each of the multiple products found from the first online shop and at least one other online shop different from the first online shop S1140. Specifically, the automated device 1000 can compare the first temporary data of the stored final product data with the data of similar products found, and extract the differences between the multiple products found from the first online shop and at least one other online shop different from the first online shop. Then, it can generate option data by linking the extracted differences with pre-set option classification criteria.

[0096] Then, the automated device 1000 can use the generated option data to generate an option page S1150 in which multiple searched products are configured as different options for a single product. In other words, it can generate an option page with a function that allows the user to select the product corresponding to each option based on the generated option data.

[0097] The automated device 1000 may also determine and add the sales price while generating the options page. The automated device 1000 may set an option price for each product corresponding to each option based on the price at which the corresponding product is sold in the online shop that sources the product, shipping costs by product category, exchange rates, margins, payment processing fees, and information on the shipping agent. Next, the system may determine the list price and sales price to be displayed on the options page based on the set option price, although the sales price may be set to be less than or equal to the option price.

[0098] Furthermore, the automated device 1000 may translate the options page. Specifically, the automated device 1000 may select one of several trained translation models based on the category of the target product for which the options page was generated. Then, it may translate the options page using the selected trained translation model. The automated device 1000 may combine the translated options page with the product page containing header content (images, audio, video, etc.) files, footer content (images, audio, actions, etc.) files, and saved final product data to generate and save a product detail page. When generating the product detail page, the automated device 1000 may identify and remove images or text related to price present on the product page containing the saved final product data.

[0099] Figure 12 is a flowchart illustrating a control method for an automated device 1000 according to one embodiment of the present invention. In particular, Figure 12 illustrates a method for automating the process of registering products in an open market. According to Figure 12, the automated device 1000 can generate a data format according to the respective requirements for registration in at least one open market S1210. The automated device 1000 can then convert product data, including a pre-stored product detail page, into JSON format S1220, and apply the converted product data to the generated data format to generate data that allows for product registration S1230.

[0100] Next, the automated device 1000 can use the generated product registration progress data to register the product on behalf of the seller in the corresponding open market (S1240). More specifically, once the product registration progress data is generated, the automated device 1000 can transmit authentication for product registration to the seller's seller terminal. The automated device 1000 then compares the key value received from the seller's terminal with the key value stored in the database to perform authentication, and once authentication is complete, it can register the product in the open market. Once the automated device 1000 receives confirmation from the open market server that product registration is complete, it can link and save the registered product and the seller (S1250).

[0101] Furthermore, after product registration is complete, the automated device 1000 may perform product registration initialization after a predetermined period has elapsed. Specifically, the automated device 1000 may start initialization by deleting all registered products from the open market. Next, the automated device 1000 may classify the products stored in the database into products that have been sold, products that have not been sold, and newly sourced products. Then, the automated device 1000 may adjust the proportion of products in each classification according to the distribution model and perform product registration again. For example, the distribution model may be a trained model that takes at least one of the following as input values: sales frequency, set sales price, and the subscription period of the seller, and outputs the proportion of product distribution that maximizes total sales.

[0102] Figure 13 is a flowchart illustrating a control method for an automated device 1000 according to one embodiment of the present invention. In particular, Figure 13 illustrates a method for automating the operation of processing a large number of orders coming in from multiple open marketplaces. Referring to Figure 13, when an order signal is received from at least one open marketplace server, the automated device 1000 can determine whether the inventory quantity of the product corresponding to the received order signal is equal to or greater than the order quantity S1310. If the inventory quantity is less than the order quantity, the automated device 1000 can cancel the order. Conversely, if it is determined that the inventory quantity of the product is equal to or greater than the order quantity, the automated device 1000 can calculate a margin S1320. Specifically, the automated device 1000 can receive price information of the product to be sold at the online shop where the inventory quantity of the product has been confirmed, and can calculate a margin by comparing the received price information of the product with the registered selling price. The automated device 1000 can then determine whether the calculated margin satisfies the pre-set conditions.

[0103] If the calculated margin satisfies the pre-set conditions, the automated device 1000 can change the state of the received order signal to an orderable state S1330. Conversely, if the calculated margin does not satisfy the pre-set conditions, the automated device 1000 can cancel the order corresponding to the received order signal.

[0104] Once it is confirmed that the status has been changed to "orderable," the automated device 1000 may check if a value corresponding to the individual customs clearance code exists. If it is determined that a value corresponding to the individual customs clearance code exists, the automated device 1000 may change the status from "orderable" to "ready for delivery." Conversely, if it is determined that a value corresponding to the individual customs clearance code does not exist, the automated device 1000 may wait until a value corresponding to the individual customs clearance code is entered. Once it is confirmed that the status has been changed to "ready for delivery," the automated device 1000 may approve the order and transmit information about the approved goods to the seller's terminal.

[0105] For other methods, such as automating the process of collecting orders from various open marketplaces and purchasing products from each online shop, automating the process of confirming product delivery, and automating customer service (CS) processing, including return requests and inquiries, please refer to the description of the automated device 1000 mentioned above.

[0106] The methods described above can be embodied in the form of program instructions that can be performed via various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., individually or in combination. The program instructions recorded on the medium may be specifically designed and configured for the present invention, or may be known and usable by those skilled in the art of computer software. Computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magnetooptical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memory. Examples of program instructions include not only machine code, such as that produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

[0107] As described above, although this disclosure has been explained by limited embodiments and drawings, this disclosure is not limited to the embodiments described above, and a person with ordinary skill in the art to which this disclosure belongs can make various modifications and variations from this description. Therefore, the scope of this disclosure should not be limited to the embodiments described above, but should be determined not only by the claims described below, but also by equivalent claims. [Explanation of Symbols]

[0108] 1000: Automated product sourcing equipment 10: Database 20: Communications Department 30: Processor 1100: Sourcing Module 1200: Machining Module 1300: Registration Module 1400: Order Processing Module 1500: Purchased Module 1600: Delivery Module 1700: CS Module 2000: Open market server 3000: Server of the shipping agent 4000: Online shop server 5000: Seller terminal 6000: Administrator terminal

Claims

1. In automated equipment for cross-border e-commerce, A database for storing data, A processor that controls the automation device, The aforementioned processor, An automated device that, while scraping web pages, identifies pre-set words indicating popular search keywords, extracts keywords that have been searched more than a pre-set frequency and saves them in the database, searches a sales platform using the extracted and saved keywords, saves the URL addresses of online shops found on the sales platform in the database, and saves product data of the online shops in the database using the saved URL addresses of the online shops.

2. The aforementioned processor, The automated device according to claim 1, wherein, once a pre-set word indicating the aforementioned popular search keyword is identified, the scrapped web page is analyzed to find the associated search engine, and keywords that have been searched more than a pre-set frequency through the found search engine are extracted and stored in the database.

3. The aforementioned processor, The automated device according to claim 1, which uses the URL address of the online shop that has been stored to store product data of the online shop as first temporary data in the database, compares the stored first temporary data with infringement judgment data, and if the first temporary data corresponds to the infringement judgment data as a result of the comparison, determines that the data cannot be sold and deletes it from the database.

4. The aforementioned infringement determination data is, This includes deleted data and pre-set prohibited word data based on trademarked words, The aforementioned processor, The automated device according to claim 3, which controls the database to add the first temporary data, which is determined to be unsaleable data, to the deleted data.

5. The aforementioned processor, The automated device according to claim 3, which searches for similar products using images of products that constitute the first temporary data, and controls the database to include data relating to the searched similar products in the first temporary data and save it as final product data.

6. The aforementioned processor, The automated device according to claim 5, which uses the stored final product data to generate option data to be attached to each of the multiple products searched from the first online shop and at least one online shop different from the first online shop, and uses the generated option data to generate an option page that configures the multiple products searched as different options for a single product.

7. The aforementioned processor, The automated device according to claim 6, which compares the first temporary data of the stored final product data with the data of similar products that have been searched, extracts differences between a plurality of products searched from the first online shop and at least one online shop different from the first online shop, links the extracted differences to a pre-set option classification criterion, and generates option data based on the linked option classification criterion.

8. The aforementioned processor, The automated device according to claim 6, which generates an option page with a function that allows the user to select a product corresponding to each option based on the generated option data, sets an option price for each product corresponding to each option based on the price at which the product is sold in the online shop that sources the product, shipping costs by product category, exchange rates, margins, payment processing fees, and shipping agent information, and determines the list price and selling price to be displayed on the option page based on the set option price.

9. The aforementioned processor, The automated device according to claim 6, which selects one of several trained translation models based on the category of the target product for which the option page was generated, translates the option page using the selected trained translation model, and generates and saves a product detail page by combining the translated option page with a product page containing a header content file, a footer content file, and the saved final product data.

10. The aforementioned processor, The automated apparatus according to claim 9, which identifies images or text relating to price present on product pages included in the stored final product data, and removes the identified images or text.

11. In a control method for automated equipment for cross-border e-commerce, Steps to scrap a webpage, If a pre-defined word indicating a popular search keyword is found during the aforementioned scrapping process, the keyword that has been searched more than the pre-defined frequency is extracted and saved. The steps include: searching the sales platform using the extracted and saved keywords; The steps include saving the URL address of the online shop found through the aforementioned sales platform, A method for controlling an automated device, comprising the step of saving product data of an online shop using the URL address of the online shop that has been saved.

12. The step of extracting and saving the searched keywords is: Once the pre-set words indicating the aforementioned popular search keywords are identified, the scrapped web pages are analyzed to find the associated search engines. A method for controlling an automated device according to claim 11, comprising the step of extracting and saving keywords that have been searched more than a predetermined frequency via the search engine found.

13. The step of saving the product data of the aforementioned online shop is: The steps include saving the product data of the online shop as first temporary data using the URL address of the online shop that has been saved, The steps include comparing the previously saved first temporary data with the infringement determination data, A control method for an automated device according to claim 11, comprising the step of determining that the first temporary data corresponds to the infringement judgment data as a result of the comparison and deleting it.

14. The aforementioned infringement determination data is, This includes deleted data and pre-set prohibited word data based on trademarked words, The aforementioned deletion step is, A control method for an automated device according to claim 13, further comprising the step of adding the first temporary data, which is determined to be unsaleable data, to the deleted data.

15. The step of saving the product data of the aforementioned online shop is: The steps include: searching for similar products using images of products that make up the aforementioned first temporary data; A method for controlling an automated device according to claim 13, further comprising the step of including data relating to the searched similar products in the first temporary data and saving it as final product data.

16. The steps include generating option data to be attached to each of the multiple products searched from the first online shop and at least one other online shop using the aforementioned saved final product data, A method for controlling an automated device according to claim 15, further comprising the step of generating an option page that uses the generated option data to configure the searched items as different options for a single item.

17. The step of generating the aforementioned option data is: The steps include comparing the first temporary data of the stored final product data with the data of similar products that were searched, and extracting the differences between the first online shop and multiple products searched from at least one online shop different from the first online shop, The steps include: linking the extracted differences to pre-defined optional classification criteria; A method for controlling an automated device according to claim 16, comprising the step of generating option data based on the linked option classification criteria.

18. The step of generating the aforementioned options page is: The steps include generating an option page that has a function to select a product corresponding to each option based on the generated option data, The steps include setting the option price for each option based on the price at which the corresponding product is sold on the online shop that sources the product, shipping costs by product category, exchange rates, margins, payment processing fees, and information about the shipping agent, and A method for controlling an automated device according to claim 16, comprising the step of determining a list price and a selling price to be posted on the option page based on the set option price.

19. The aforementioned options page includes the step of selecting one of several trained translation models based on the category of the target product, The steps include: translating the options page based on the selected trained translation model; A method for controlling an automated apparatus according to claim 16, further comprising the steps of generating and saving a product detail page by combining the translated option page with a product page containing a header content file, a footer content file, and the saved final product data.

20. The step of generating and saving the product details page is: The steps include identifying an image or text related to the price present on the product page included in the aforementioned saved final product data, A method for controlling an automated apparatus according to claim 19, comprising the step of removing the identified image or text.

Images