Web Aggregation System Using Community Data for Dynamic Access
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Solution Overview
Problem
Existing account aggregation systems are cumbersome and time-consuming as they navigate websites in a static, programmed fashion, struggling with dynamic situations that require user interaction, such as authentication with varying interactivity requests.
Innovation Solution
A system that facilitates access to websites by obtaining community data to identify interactivity requests, using user-specific data including identifiers and passwords to automate access, and employing interactivity objects to verify identities and perform tasks, thereby automating navigation, data extraction, and content aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing aggregation systems navigate websites in a static, programmed fashion, then the system structure is simple and predictable, but the system cannot handle dynamic situations requiring user interaction
Solution Approach 1:
The patent applies dynamics by transitioning from static navigation scripts to dynamic navigation patterns that adapt to website changes. The system learns navigation patterns from community data and adjusts its behavior dynamically to handle different website structures and interactivity requests, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system uses feedback mechanisms by analyzing community data from multiple users to identify and learn navigation patterns. This feedback loop allows the system to continuously improve its navigation strategy based on real-world usage, enabling it to handle dynamic situations while maintaining manageable complexity through collective learning.
2Extent of automation
If the system requires user interaction for authentication, then security and adaptability are improved, but the automation process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-authenticating users and pre-loading navigation patterns before actual website access is needed. User authentication and navigation strategies are prepared in advance based on community data, allowing the system to automate subsequent access without requiring real-time user interaction, thus reducing time loss while maintaining security.
Solution Approach 2:
The system uses copying by replicating successful navigation patterns from community data. Instead of requiring each user to manually navigate or authenticate, the system copies proven navigation strategies from the community, enabling automated access while minimizing the time needed for user-specific authentication and interaction.
3Ease of operation
If the system collects and analyzes community data to identify interactivity requests, then the system's ability to automate access is improved, but the data processing complexity increases
Solution Approach 1:
The system merges similar data processing tasks by combining multiple users' navigation and authentication data into unified navigation patterns. Instead of processing each user's data separately, the system aggregates and merges community data to identify common interactivity requests, reducing overall processing complexity while improving automated access ease.
Solution Approach 2:
The system applies universality by creating multi-functional navigation patterns that can handle multiple user types and website scenarios. The same navigation pattern framework serves different purposes across various websites and user cases, reducing data processing complexity through generalization while maintaining ease of operation for specific automated access tasks.
Data Source
AI summary
Some embodiments of the present invention provide a system that facilitates access to a website from an application. During operation, the system obtains community data associated with interactions between a set of users and the website and examines the community data to identify an interactivity request made by the website to users of the website. Next, the system obtains user-specific data from a new user of the application, which includes a response to the interactivity request from the new user. Finally, the system uses the user-specific data to automate access to the website for the new user.