Contextual Search Term Highlighting in Running Applications
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Solution Overview
Problem
Users face the challenge of needing to manually enter query strings for searches within applications, without receiving suggestions for relevant terms being discussed in their context, such as movies or restaurants, which can be time-consuming and inefficient.
Innovation Solution
A method and apparatus that links application terms to predictive search queries by receiving key terms from a key terms server, determining matching terms, and requesting search queries from a search server, with the ability to highlight and overlay search results within the application interface, using unambiguous and ambiguous key terms with contextual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually enter query strings for searches, then search functionality is available, but search efficiency and time consumption are poor
Solution Approach 1:
The system performs preliminary action by proactively generating and presenting search query suggestions to users before they need to manually enter queries. The suggestion module analyzes application data and pre-computes relevant search terms, displaying them for user selection, thereby eliminating the time-consuming manual query entry process while maintaining search functionality
Solution Approach 2:
The system implements self-service by automatically analyzing application data, generating search suggestions, and presenting them to users without requiring manual input. The suggestion module autonomously processes application content, identifies relevant search terms, and provides ready-to-use query options, allowing users to simply select from pre-generated suggestions rather than constructing queries from scratch
2Loss of information
If the system provides search suggestions based on application context, then search relevance is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the search suggestion functionality into distinct modular components: a suggestion module that analyzes application data and generates candidates, a selection module that presents options to users, and an integration layer that connects to the application framework. This modular architecture reduces system complexity by allowing each component to be developed, maintained, and optimized independently while collectively providing contextually relevant search suggestions
3Ease of operation
If the system overlays search results within the application interface, then user experience is improved, but interface complexity increases
Solution Approach 1:
The system merges the search results display with the existing application interface by overlaying search results directly within the current application context. Instead of requiring users to switch to a separate search interface or application, the system integrates search functionality into the existing UI framework, combining information display and search operations in a unified interface that improves ease of operation while managing complexity through shared UI components
Data Source
AI summary
A machine-readable medium that gathers a plurality of key terms is described. In an exemplary embodiment, the machine-readable medium receives a plurality of terms and selects a plurality of key terms from the plurality of terms, wherein each of the plurality of key terms is a term that is highlighted in a running application. The machine-readable medium further associates a predictive search query for each of the plurality of key terms, wherein the predictive search query is executed when that term is found in a running application and the user selects this term in the running application. The machine-readable medium additionally sends the plurality of key terms to a plurality of devices, wherein each of the plurality of devices matches at least one of the plurality of key terms in that device.