Machine-Learning Website Navigation for Journey Compliance Testing
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Solution Overview
Problem
Users and service providers face challenges in determining if a web storefront meets security and quality of service expectations due to varying functionalities across websites, making it difficult to ensure compliance with agreed terms and user satisfaction.
Innovation Solution
A method and system using a machine learning-based website navigation system to navigate through a complex hierarchy of web pages, measuring parameters such as security vulnerabilities, functionality, and user journey compliance by defining functional steps and verifying requirements, and utilizing a database for verified data field information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning-based automated navigation system is used to evaluate websites, then measurement precision and productivity are improved, but device complexity increases
Solution Approach 1:
The system creates a virtual copy of the website by navigating through its pages using a trained machine learning model. This digital replica allows comprehensive measurement of website parameters without requiring physical access or manual inspection, thereby improving measurement precision while the automated nature reduces operational complexity
Solution Approach 2:
The patent replaces manual website evaluation with an automated machine learning-based navigation system. The ML model learns website navigation patterns and automatically traverses pages to collect data, substituting human operators with an automated system that provides more consistent and precise measurements
2Reliability
If comprehensive website parameter measurement is performed, then reliability of assessment is improved, but loss of time increases
Solution Approach 1:
The machine learning navigation system continuously monitors and measures website parameters throughout the entire user journey without interruption. By maintaining continuous action from landing page to destination screen, the system achieves comprehensive reliability while eliminating idle time between measurement steps
Solution Approach 2:
The system performs preliminary navigation and data collection during the website evaluation process itself, rather than requiring separate analysis phases. The ML model anticipates necessary measurements and collects them proactively as part of the navigation flow, reducing overall time while maintaining comprehensive assessment
3Productivity
If automated navigation through complex website hierarchies is implemented, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The machine learning navigation system incorporates feedback mechanisms where the model continuously monitors website content, user interactions, and navigation patterns. This feedback loop enables the system to adapt to complex website structures in real-time, maintaining high productivity while accurately detecting difficult-to-measure parameters through iterative refinement
Data Source
AI summary
A method of measuring parameters associated with a website comprising a complex hierarchy of web pages is described. The method is used for a website adapted to support a user journey through multiple web pages to a user destination. The user journey is defined as a sequence of functional steps. A machine learning system is trained to provide a website navigation system adapted to navigate from a landing screen to a destination screen by performing each functional step in the sequence. The website is then navigated from the landing screen to the destination screen using the website navigation system. One or more parameters resulting from, or associated with, one or more steps of the journey from the landing screen to the destination screen for the website are measured. A computing system suitable for performing the method, and a method of building a suitable website navigation system are also described.