Targeted Anti-Malware Scan via User Symptom Feedback
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Solution Overview
Problem
Current anti-malware protection systems are inefficient due to lengthy scan times, which negatively impact user experience, and lack mechanisms to incorporate user feedback regarding system malfunctions.
Innovation Solution
A system that solicits user feedback on system malfunctions through a user interface, uses a symptom parser engine to determine targeted scan parameters, and performs a targeted anti-malware scan, thereby reducing the amount of data to be scanned and improving responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full system anti-malware scan is performed to ensure comprehensive coverage, then detection reliability is improved, but scan time increases significantly
Solution Approach 1:
The patent segments the full system scan into targeted scan portions based on user symptom feedback. Instead of scanning the entire system, the anti-malware software identifies and scans only specific files, folders, or system areas most likely to contain malware based on the reported symptoms, thereby reducing scan time while maintaining detection reliability for the suspected areas.
Solution Approach 2:
The patent applies local quality by customizing the scan scope according to user-specific symptoms. Different users provide different symptom feedback, and the system adjusts the scan parameters accordingly to focus on locally relevant areas. This allows each user to receive a tailored scan that addresses their specific concerns rather than performing a uniform full-system scan.
2Ease of operation
If current anti-malware systems perform standard scans without user feedback, then operational simplicity is maintained, but scan effectiveness decreases
Solution Approach 1:
The patent implements a feedback mechanism where users provide symptom feedback about system issues, and this feedback is used to adjust scan parameters. The system collects user input, processes it to identify relevant scan targets, and configures the scan accordingly. This feedback loop significantly improves scan effectiveness by focusing resources on areas most likely to contain malware, while the user interface remains simple and intuitive.
3Reliability
If the system scans all files and folders to ensure comprehensive malware detection, then detection completeness is improved, but user experience deteriorates due to long wait times
Solution Approach 1:
The patent applies preliminary action by analyzing user symptom feedback before initiating the scan to pre-identify the most likely malware locations. The system processes user input, determines priority scan areas, and configures the scan strategy in advance. This preliminary analysis ensures that when the scan executes, it focuses on high-probability targets, reducing wait time while maintaining detection completeness for the suspected areas.
Solution Approach 2:
The patent implements dynamics by making the scan scope flexible and adaptable rather than fixed. The scan parameters dynamically adjust based on user symptom feedback, allowing the system to respond to different user situations. This dynamic approach enables the system to balance detection completeness with user experience by scanning only what is necessary based on the specific symptoms reported.
Data Source
AI summary
Apparatus, systems, articles of manufacture, and methods for improving anti-malware scan responsiveness and effectiveness using user symptoms feedback are disclosed. An example method includes detecting a performance issue on a computing device, presenting a user interface on a display of the computing device requesting user feedback regarding the performance issue, and synthesizing user input related to the performance issue to identify, on the computing device, a scan parameter associated with the performance issue. The example method further includes, in response to failing to identify the scan parameter on the computing device, transmitting the user input to a symptom analysis server to identify the scan parameter based on anti-malware scans from other computing devices, and, in response to determining the scan parameter, performing a targeted anti-malware scan on the computing device.