Browser Overlay Code Testing for Automatic Website Discount Selection
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Solution Overview
Problem
Consumers face challenges in identifying and applying effective discount codes during online shopping, as existing systems require manual effort and often result in using outdated or ineffective codes, leading to suboptimal pricing outcomes.
Innovation Solution
A computer-implemented system that automatically applies discount codes on behalf of consumers by integrating a browser extension, which retrieves and optimizes code application based on configuration information and machine learning algorithms to identify the best code for a given electronic commerce platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consumers manually search and apply discount codes, then they can access available codes, but they often use outdated or ineffective codes resulting in suboptimal pricing
Solution Approach 1:
The system automatically applies discount codes without requiring manual consumer intervention. The computer-implemented system retrieves available codes, evaluates their effectiveness, and applies the optimal code automatically, allowing the system to serve itself rather than requiring consumers to manually search and apply codes.
Solution Approach 2:
The system continuously monitors code effectiveness by tracking whether applied codes are successful and measuring the price reduction achieved. This feedback loop allows the system to learn from past applications and improve future code selection, ensuring higher reliability in code effectiveness over time.
2Reliability
If consumers manually identify and test multiple codes, then they might find working codes, but it requires significant time and effort
Solution Approach 1:
The system performs preliminary retrieval and evaluation of discount codes before the consumer needs to make a purchase. By proactively gathering and pre-testing codes in advance, the system eliminates the need for consumers to spend time manually searching and testing codes at the moment of purchase.
Solution Approach 2:
The manual mechanical process of consumers typing codes, copying, pasting, and testing multiple options is replaced by an automated computer system that programmatically retrieves, evaluates, and applies the optimal code, significantly reducing the time and effort required.
3Reliability
If consumers try multiple codes to find the best discount, then they might achieve better pricing, but they often stop after a few attempts missing better options
Solution Approach 1:
The system uses feedback from previous code applications to continuously improve its selection algorithm. By analyzing which codes work and what price reductions they achieve, the system optimizes its future code selection to maximize pricing benefits, ensuring consumers receive the best possible deal without manual trial and error.
Solution Approach 2:
The manual process of consumers systematically testing multiple codes is replaced by an automated system that efficiently evaluates numerous codes and applies the optimal one, dramatically improving code application efficiency while ensuring thorough testing of available options.
4Quantity of substance
If Code Aggregators provide extensive code lists, then consumers have more code options, but it becomes difficult to identify working and relevant codes
Solution Approach 1:
The manual task of consumers visually scanning through extensive code lists to identify working and relevant codes is replaced by an automated computer system that programmatically retrieves, evaluates, and selects the optimal code from large datasets, eliminating the difficulty of manual code identification.
Solution Approach 2:
The system uses feedback from code application results to continuously refine its selection criteria. By learning from successful and unsuccessful code applications, the system improves its ability to identify relevant and working codes from large lists, making code selection more accurate and reliable.
Data Source
AI summary
Computer systems and methods for interfacing with a third party website, comprising determining that a webpage of the third party website is being output in an interface of a web browser, and in response, generating an interface element that at least partially overlaps the webpage in the interface. The interface element includes a user-selectable graphical trigger that, when selected, causes digital codes to be automatically input into a data entry interface identified in the webpage, thereby triggering the third party website to receive each of the digital codes and to return a respective response. Based on monitoring the respective response to each of the digital codes, a particular digital code is identified that causes a greatest change in a numerical value displayed in the webpage. The particular digital code is applied to obtain a resulting numerical value that is displayed in the webpage.