Personalized Text Preview System for Workflow Continuity
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Solution Overview
Problem
Existing solutions fail to provide user-specific previews for unfamiliar terms within text, disrupting workflow and not adequately addressing personalized information needs, as they require manual selection and navigation to separate web pages, and often provide static previews that do not account for individual user behavior.
Innovation Solution
A system that tracks user search behavior across various applications, uses a machine-learning model to categorize and rank candidate terms for personalized previews, and integrates search results directly into the text, allowing users to hover over terms for context without leaving their workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user manually selects text and navigates menu options to search for unfamiliar terms, then the user can obtain information about the term, but the user's workflow is disrupted and time is lost
Solution Approach 1:
The system performs preliminary actions by automatically detecting unfamiliar terms in the text and pre-fetching search results before the user even requests them. This eliminates the need for manual text selection and menu navigation, as the information is already prepared and ready for immediate display upon user interaction.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the text and search results. Instead of requiring direct user interaction with search menus, the intermediary automatically generates and displays preview cards with search results directly within the application context, streamlining the information retrieval process.
2Loss of information
If a user is navigated to a separate webpage to search for term information, then comprehensive search results can be obtained, but the user's current workflow and context are interrupted
Solution Approach 1:
The system extracts the essential search results and displays them directly within the current application context through preview cards. Instead of requiring navigation to a separate webpage, the relevant information is extracted and presented inline, maintaining workflow continuity while providing comprehensive search results.
Solution Approach 2:
The system transitions the search results from a separate webpage dimension to an integrated dimension within the current application. By displaying preview cards directly in the application window, the system adds a new dimension of information presentation that combines the benefits of comprehensive search results with seamless workflow integration.
3Ease of manufacture
If static previews are provided for all users, then implementation is simple, but the previews do not account for individual user behavior and needs
Solution Approach 1:
The system transitions from static previews to dynamic, adaptive previews that respond to individual user behavior. By tracking user search history and preferences, the system dynamically adjusts which terms are highlighted and what information is displayed in preview cards, making the system both personalized and adaptable while maintaining relatively simple implementation through machine learning models.
4Loss of information
If general web search is used for enterprise applications, then broad information can be found, but enterprise-specific information may not be adequately retrieved
Solution Approach 1:
The system implements a multi-functional search approach that can query multiple sources simultaneously. It combines general web search capabilities with enterprise-specific search systems, allowing the same interface to provide both broad information and enterprise-specific details based on the user's needs and context, thereby improving result relevance without sacrificing comprehensiveness.
Data Source
AI summary
Examples described herein include systems and methods for providing user-specific previews for terms within text. An example method can include receiving tracked user behavior reflecting terms selected by a user and entered into a search. A representation of known words can be created based on the tracked user behavior. By training machine-learning models for each individual user, personalized previews can be presented when each user encounters a new body of text, such as in a webpage or email. The preview can apply to a term not previously known to the user but likely to be searched by the user, relying on content gathered from a search on a search medium that the user was likely to use. The content can be presented to the user in a graphical user interface allowing for interaction and feedback.