Credential Manager DOM Traversal for Dynamic Web Forms
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Solution Overview
Problem
Credential manager applications face difficulties in identifying and locating relevant fields on web pages due to varying structures and dynamic changes, such as CAPTCHA fields and multi-factor authentication, which can lead to failed login attempts and user inconvenience.
Innovation Solution
The use of the Document Object Model (DOM) parsed by web browsers allows credential managers to efficiently locate and identify fields by traversing the DOM tree, utilizing attributes like 'id', 'name', 'class', and 'placeholder', and storing paths to these fields for future use, while avoiding ephemeral and non-unique attributes to ensure robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If credential managers use traditional form field location methods, then they can identify fields on simple web pages, but they fail to accurately locate fields on web pages with dynamic structures, CAPTCHA fields, and multi-factor authentication
Solution Approach 1:
The patent implements dynamic field location by continuously monitoring DOM changes and adapting the selection process to account for ephemeral elements like CAPTCHA fields. The system adjusts its behavior based on real-time page state, allowing it to handle dynamic web page structures and maintain reliable field identification across varying conditions.
Solution Approach 2:
The system employs feedback mechanisms by monitoring user interactions and form submission results to learn from successes and failures. When field location attempts fail, the system analyzes the feedback and adjusts its selection criteria, improving its ability to accurately identify fields on diverse web page structures over time.
2Adaptability or versatility
If credential managers traverse the entire DOM tree to locate fields, then they can handle complex web page structures, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent segments the DOM tree traversal process into multiple phases: initial rapid scanning for common field patterns, targeted deep traversal only when necessary, and prioritization based on field importance. This segmentation allows the system to handle complex structures efficiently by avoiding unnecessary traversal of entire DOM trees.
Solution Approach 2:
The system performs preliminary actions by pre-identifying common field patterns and creating prioritized lists of potential fields before actual credential entry is needed. This preliminary processing reduces the time required during actual field location operations, as the system can quickly reference pre-processed information rather than traversing the entire DOM tree from scratch.
3Ease of manufacture
If credential managers rely on static field identification methods, then they work well for stable web pages, but they fail when web pages undergo structural changes or introduce ephemeral elements
Solution Approach 1:
The patent transitions from static to dynamic field identification by implementing real-time DOM monitoring and adaptive selection. The system continuously updates its understanding of the page structure and adjusts field identification accordingly, maintaining reliability even when web pages undergo structural changes or introduce ephemeral elements like CAPTCHA fields.
Solution Approach 2:
The system incorporates feedback loops that monitor the effectiveness of field identification and learn from successes and failures. When static methods fail due to page changes, the feedback mechanism enables the system to adapt its identification strategies, maintaining high reliability across evolving web page structures.
4Productivity
If credential managers attempt to fill all detected fields, then they maximize coverage of credential requirements, but they waste time on ephemeral fields like CAPTCHA that should be ignored
Solution Approach 1:
The patent implements feedback-based filtering where the system monitors form submission results and user interactions to identify which fields are actually required for credential entry. By learning from feedback, the system can distinguish between meaningful credential fields and ephemeral elements like CAPTCHA, filtering out the latter to maintain both speed and accuracy.
Solution Approach 2:
The system applies local quality analysis by evaluating each detected field's characteristics and context individually rather than treating all fields uniformly. This allows the system to identify and prioritize fields that are likely to be credential entries based on their local properties (such as field type, surrounding context, and interaction patterns) while filtering out ephemeral elements.
Data Source
AI summary
Techniques allow identification of credential fields in a credential form on a web page that can be stored in a credential manager database to allow a credential manager application to fill the credential fields with saved credentials managed by the credential manager.