Web Application Usability Feedback Overlay System
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Solution Overview
Problem
Current methods for addressing usability problems in Web applications are time-consuming and labor-intensive, as they rely on individual developers to collect feedback, which is often done infrequently and analyzed on a per-app basis, leading to delayed resolution of issues.
Innovation Solution
A system that collects user performance and navigation feedback across multiple client devices and Web application sessions using machine learning, providing contextual pop-up instructions to users based on typical user behavior, thereby rapidly identifying and alleviating usability issues across all Web and SaaS applications within an organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual developers collect and analyze feedback on a per-app basis, then feedback can be application-specific, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent merges feedback collection and analysis across multiple Web applications into a unified system. The server aggregates user feedback data from various applications and uses machine learning to analyze patterns collectively, resolving issues systematically rather than individually, thus reducing time and effort while maintaining accuracy
Solution Approach 2:
The system implements continuous feedback loops where user interactions are monitored, analyzed through machine learning, and used to automatically generate fixes or recommendations. This automated feedback mechanism eliminates manual analysis delays while preserving application-specific feedback quality
2Ease of operation
If feedback is collected infrequently and analyzed individually, then developer workload is reduced, but issue resolution is delayed
Solution Approach 1:
The system enables self-service through automated machine learning analysis that continuously processes user feedback without requiring manual developer intervention for each issue. The server autonomously identifies patterns, diagnoses problems, and generates solutions, maintaining low developer workload while achieving rapid issue resolution
Solution Approach 2:
The system performs preliminary analysis of user feedback in real-time as it arrives, rather than waiting for periodic manual review. Machine learning models continuously process data to identify and resolve issues before they accumulate, increasing productivity without burdening developers
3Device complexity
If manual feedback collection is used, then implementation is simple, but the system cannot provide rapid identification and mitigation of usability problems across multiple applications
Solution Approach 1:
The patent replaces manual mechanical feedback collection processes with automated machine learning systems. The server uses AI algorithms to automatically collect, analyze, and process user feedback across multiple applications, transforming the simple but inefficient manual process into a complex yet highly productive automated system that rapidly identifies and mitigates usability problems
Data Source
AI summary
A computing device may include a memory and a processor cooperating with the memory to communicate with a plurality of client devices, and determine a problem with a Web application based upon received data from the client devices. The processor may further cause at least one of the plurality of client devices to display a graphical overlay over a Web application, with the graphical overlay including content related to the determined problem.