Method for Assisted Order Handling Via the Internet

Abstract

The invention relates to a method for enabling order handling via the Internet, wherein the method is performed in a computer system comprising at least a client, an ordering server and an agent server connected to the Internet, each of the client, the ordering server and the agent server comprising at least one processor and a memory storing one or more programs for execution by the at least one processor. The invention discloses a method comprising: capturing at least a part of the contents of a website retrieved by a web browser on the client from the ordering server; transferring the captured contents to the agent server; deriving object information from the captured contents on the agent server to identify an object to be ordered; controlling the ordering server by the agent server to order the object.

Claims

1. Method for enabling order handling via the Internet, wherein the method is performed in a computer system comprising at least a client, an ordering server and an agent server connected to the Internet, each of the client, the ordering server and the agent server comprising at least one processor and a memory storing one or more programs for execution by the at least one processor, the method comprising: capturing at least a part of the contents of a website retrieved by a web browser on the client from the ordering server; transferring the captured contents to the agent server; deriving object information from the captured contents on the agent server to identify an object to be ordered; controlling the ordering server by the agent server to order the object.

2. Method of claim 1, wherein the ordering server is a vendor server running an online shop, wherein the object is a product or service to be purchased from the online shop.

3. Method of claim 1, wherein the object information is communicated to the client where the object information is presented to a user of the client, and wherein the ordering of the product is performed after receiving an ordering approval from the client.

4. Method of claim 1, wherein the capturing of the contents and the transferring to the ordering server is initiated by activating a plugin of the web browser on the client.

5. Method of claim 1, wherein the captured contents are converted into a standardized data format prior to transferring to the agent server.

6. Method of claim 1, wherein the contents transferred to the agent server are arranged into a document object model on the agent server.

7. Method of claim 6, wherein the digital object model is analyzed by at least one of a method of artificial intelligence and by using a knowledge data base for deriving the object information.

8. Method of claim 1, wherein order data comprising at least the object information, client information and ordering server information are placed into an order job queue.

9. Method of claim 8, wherein at least one order bot program executes orders according to the order job queue.

10. Method of claim 9, wherein the orders are executed by a number of order bot programs in parallel.

11. Method of claim 9, wherein the at least one order bot program controls the ordering server for executing each order via the web interface of the ordering server by imitating human interaction.

12. Method of claim 11, wherein methods or at least one of artificial intelligence and a knowledge data base is used for imitating the human interaction.

13. Method of claim 12, wherein the methods of artificial intelligence include at least one of heuristic rules and supervised learning.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The enclosed drawings disclose preferred embodiments of the present invention. It should be understood, however, that the drawings are designed for the purpose of illustration only and not as a definition of the limits of the invention. In the drawings:

[0022] FIG. 1 shows a diagram illustrating the method steps of the invention;

[0023] FIG. 2 shows a server architecture on which the method of the invention may be executed.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] FIG. 1 illustrates the method of the invention. In the depicted embodiment, the method comprises the following steps:

[0025] The user goes to any website, selects a product including configurations and presses a button (browser plugin).

[0026] (1) The exact state of the website will be captured and relevant information will be filtered (Extraction):

[0027] All relevant information of any website will be extracted to get the exact state of the website at the moment the users clicks the button. Spider algorithms recognize which information needs to be extracted to have a relevant data set to perform the order for the user.

[0028] (2) The information will be classified into a data format and send to a server (Conversion):

[0029] The Exact state of the website will be converted into a data format (e.g. JSON) and transferred to servers.

[0030] (3) On the server the information will be classified and arranged in a virtual DOM (Arrangement):

[0031] This involves the duplication of the exact state of the website in an object-based virtual DOM (e.g. JSON DOM) on the server side. The Virtual DOM represents an object oriented tree structure of all elements of the real website the user sees. The virtual DOM is the basis to extract data from.

[0032] (4) Algorithms extract features of the virtual DOM to derive the exact order of a user (Analysis):

[0033] This involves the extraction of features/key information (e.g. product category, color, type of page, etc.) out of the Virtual DOM via proprietary algorithms.

[0034] Algorithms are aligned with a self-learning, proprietary dictionary.

[0035] Extraction requests are performed on a decentralized, scalable and service oriented server architecture.

[0036] (5) The order will be summarized (e.g. via a pop-up) and shown to the user (Representation):

[0037] The extracted features will be converted into a virtual representation (frontend). The user sees a pop-up of the product order.

[0038] (6) As soon as the order is recognized, the order will be placed into a job queue (Queuing):

[0039] Product orders will be put into a job queue organized by certain characteristics and can be flexibly approached via different bots.

[0040] (7) Bots will now execute the orders with an intelligent logic and imitate human activity (Action):

[0041] The bots receive information to take action. The bot follows a certain action plan based on the URL, e.g. buy product, include user data, click selectables. The action plan is aligned with an intelligent shopping map and the extracted features in step 4.

[0042] (8) As long as the order is not finally executed, step (1) to (7) with the exception of (5) will be repeated:

[0043] As long as the purchase of the customer is not finally executed the bots continue to work. Step 1 until 8 repeats (with exception of step 5 that only happens once to show the user the Pop-Up).

[0044] With reference to FIG. 2, the server infrastructure is based on a service oriented architecture. Every service registers itself to a central service discovery. Every service can then discover a service and connect to it in order to create remote procedure calls. Every new order also creates a new job that is stored inside the job queue. The bots are querying for new jobs and perform those if new ones exist. The extraction process is separated into multiple server instances to parallelize and speed up the data extraction.