Contextual Search Query Identification for Proactive Enterprise Search
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Solution Overview
Problem
Conventional search technologies require explicit user input for search queries, which can be cumbersome and impractical in real-time interactions, such as electronic meetings.
Innovation Solution
A computing system performs proactive searches by analyzing real-time conversations or document interactions, determining search intent through natural language processing, and presenting relevant content without explicit user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search technologies require explicit user input for search queries, then search accuracy can be maintained, but user convenience and operational ease deteriorate
Solution Approach 1:
The system performs preliminary analysis of user activity patterns, document access history, and contextual information to proactively generate search queries before the user explicitly requests search functionality. This preliminary action enables the system to anticipate user needs and present relevant content without requiring manual query formulation, thereby improving ease of operation while maintaining search accuracy.
2Speed
If proactive search is performed without explicit user indication, then search responsiveness improves, but system complexity increases
Solution Approach 1:
The system implements a multi-functional search module that simultaneously performs proactive content retrieval, contextual analysis, and user activity monitoring within a single integrated framework. This universal approach enables fast search response by leveraging existing computational resources for multiple purposes, reducing the need for separate complex systems while maintaining high responsiveness.
Solution Approach 2:
The search system performs self-service by automatically analyzing user behavior patterns, selecting relevant content, and presenting search results without requiring continuous user intervention or complex manual configuration. This self-service capability reduces operational complexity while enabling rapid response to user needs through automated decision-making based on learned preferences and contextual factors.
3Loss of information
If real-time search is performed during conversations, then information accessibility improves, but interaction disruption increases
Solution Approach 1:
The system introduces search results as an intermediary element that enriches the conversation without directly interrupting the interaction flow. Relevant information is presented through contextual overlays, side panels, or subtle UI enhancements that provide additional value while maintaining the primary conversation thread, thereby improving information accessibility without causing harmful disruption to user interaction.
Data Source
AI summary
A computing system obtains text that relates to an experience of a user and determines a search intent based upon the text and a context of the user, where the context is determined based upon activity history of the user in a plurality of applications. The computing system identifies potential keywords in the text and identifies a search domain in a plurality of search domains based upon the potential keywords. The computing system computes a confidence score for each of the potential keywords based upon the search domain, the context, and prior search queries of the user. The computing system identifies keywords from amongst the potential keywords based upon the confidence scores and executes a search over an index based upon the keywords, where the index indexes user content of the user and content of an enterprise. The computing system presents search results for the search to the user.