Prioritized Search Results Based on Monitored Usage Data
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Solution Overview
Problem
Existing search systems on mobile devices are cumbersome due to small screen sizes, requiring extensive scrolling and lacking prioritization or selectivity in search results, which increases search time and processing resources.
Innovation Solution
An enhanced search system that monitors application usage to prioritize and select data items from frequently, recently, or extensively used applications, reducing the search scope and display area, and implementing a progressive reveal of search results to prioritize relevant entries first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a typical search system displays all search results on a mobile device, then the user can access all matching data items, but the small screen size requires extensive scrolling and increases search time
Solution Approach 1:
The search system automatically monitors application usage patterns and performs prioritization without user intervention. The system self-adjusts the search scope and result ordering based on learned user behavior, eliminating the need for users to manually filter or scroll through all results.
Solution Approach 2:
The system pre-monitors application usage data and pre-establishes priority rankings before the search is executed. This preliminary collection of usage statistics allows the system to quickly prioritize results during the actual search operation, reducing display time.
2Adaptability or versatility
If a typical search system searches all applications, then the user can find data from any source, but the search scope is too large for small form-factor devices
Solution Approach 1:
The system extracts and prioritizes data from frequently used applications, separating these high-value results from the rest of the search space. By pulling out only the most relevant applications based on usage monitoring, the system reduces the effective search scope while maintaining access to diverse data sources.
Solution Approach 2:
The system applies different search strategies to different applications based on their usage importance. Frequently used applications receive prioritized search treatment, while less important applications are searched less intensively or not at all, creating a non-uniform search quality that matches user needs.
3Ease of operation
If search results are displayed without prioritization, then all data items are treated equally, but the user must scroll extensively to find relevant results
Solution Approach 1:
The system changes the ordering parameter of search results from alphabetical or relevance-based to usage-frequency-based. By transforming the sorting criterion to reflect actual user behavior patterns, the system makes frequently accessed data appear first, reducing the display area needed to find important results.
4Measurement precision
If the search system monitors all application usage, then the prioritization is accurate, but the processing resources are increased
Solution Approach 1:
The system monitors usage of only the most important applications in detail, rather than all applications equally. By applying partial monitoring to high-value applications and minimal or no monitoring to low-value applications, the system achieves good prioritization accuracy with reduced processing overhead.
Data Source
AI summary
The usage of applications in a computing environment is monitored to generate data that is indicative of the application usage. The data is used to select or prioritize applications (or data items of a data type of the applications) over other applications (or data items) when a search operation is performed.