Browser Toolbar Query Refinement via Confidence Scoring
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Solution Overview
Problem
Current search engines rely heavily on user-entered search terms, which often yield inadequate or irrelevant results, leading to a time-consuming and inefficient search process for users, as they must manually refine their queries to find accurate information.
Innovation Solution
A web browser toolbar that recognizes search queries and suggests refined queries based on historical user data and search patterns, using confidence scores and co-occurrence analysis to provide more accurate search terms and vertical suggestions, allowing users to easily switch to more effective search engines and verticals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually refine search queries to find accurate information, then search accuracy improves, but time consumption increases
Solution Approach 1:
The system performs preliminary analysis of search queries using historical data and user behavior patterns before the user submits the query. By pre-computing refined query suggestions and presenting them to users, the system eliminates the need for manual query refinement iterations, thus improving search accuracy while reducing time consumption.
Solution Approach 2:
The system incorporates feedback loops where user search behavior, click-through rates, and query refinement patterns are continuously collected and used to improve future query suggestions. This feedback mechanism enables the system to learn from user interactions and provide increasingly accurate refined queries, reducing both time and effort required for effective searching.
2Ease of operation
If search engines provide more features and options in toolbars, then user experience improves, but device complexity increases
Solution Approach 1:
The patent extracts only the most essential and high-value features for the toolbar interface, such as refined query suggestions and quick search initiation, while moving complex analysis and processing to the backend server. This extraction approach maintains user experience by providing key functionalities without cluttering the interface, thus improving ease of operation while managing device complexity.
3Productivity
If the toolbar automatically refines search queries using historical data, then search effectiveness improves, but data processing complexity increases
Solution Approach 1:
The system introduces an intermediary layer between the user and the search engine that handles complex data processing. The toolbar acts as this intermediary, collecting user behavior data, coordinating with backend services for analysis, and presenting refined results. This intermediary approach enables automatic query refinement and improves search effectiveness while isolating the complexity of data processing from the user interface.
Data Source
AI summary
Embodiment described herein are generally directed to a toolbar extension of a web browser that grabs a user's search engine query and suggests a refined search query known to yield better search results. The toolbar recognizes the web page the user is on as being associated with a search engine and retrieves the user's search query. The toolbar interacts with a refinement component on a server, and the refinement component determines a refined search query based on confidence scores assigned to data mined from a data center affiliated with different search engine (one related to the toolbar). The refined search query is returned and displayed in a search field of the toolbar, allowing the user to easily run the refined search on the different search engine.