Keyword Expansion System for Dynamic Content Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face confusion and inefficiency when encountering unfamiliar terms while reading content, as they often need to open multiple browser windows and tabs to research, leading to frustration and wasted time.
Innovation Solution
A method that scans and extracts keywords from content, cross-references them with a corpus of scored keywords, and expands or abridges the content based on these scores before display, using a system comprising user devices, servers, and a corpus of scored keywords to enhance user understanding without unnecessary complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users open additional browser windows and tabs to search for unfamiliar terms, then they can understand the terms in the original content, but this results in confusion and wasted time due to managing multiple windows and tabs
Solution Approach 1:
The system performs preliminary action by pre-fetching and caching definition content for keywords before the user actually needs them. When a keyword is identified as unfamiliar, its definition is already available in the cache, allowing immediate insertion into the content without requiring the user to manually search or wait for network requests.
Solution Approach 2:
The system introduces an intermediary mechanism - a background service that monitors user reading behavior, identifies unfamiliar keywords, and automatically retrieves and inserts definitions. This intermediary handles the information search task without requiring the user to manually open multiple windows or tabs, thus resolving the contradiction between understanding terms and time management.
2Loss of information
If the system expands content with additional definitions and explanations, then user comprehension improves, but the content becomes more complex and potentially overwhelming
Solution Approach 1:
The system applies local quality by selectively expanding only the specific portions of content that contain unfamiliar keywords, rather than uniformly expanding the entire content. Definitions and explanations are inserted locally at the point where the unfamiliar keyword appears, allowing users to understand complex concepts without being overwhelmed by unnecessary expansions in familiar areas.
Solution Approach 2:
The system uses partial action by expanding content only to the extent necessary - inserting definitions and explanations only for keywords identified as unfamiliar based on user behavior analysis. This avoids excessive expansion that would make content overwhelming, while still providing sufficient information to improve comprehension of the specific unfamiliar terms.
3Adaptability or versatility
If the system analyzes and processes keywords in real-time, then content can be dynamically expanded or abridged, but this increases processing requirements and system complexity
Solution Approach 1:
The system performs preliminary action by pre-processing and caching keyword definitions during off-peak times or in the background, so that real-time content adjustment does not require intensive processing when the user is actively reading. The heavy lifting of content analysis and definition retrieval is done in advance, reducing real-time processing requirements.
Solution Approach 2:
The system implements self-service by using automated machine learning models and algorithms that independently analyze user behavior, identify unfamiliar keywords, and determine appropriate content expansions without requiring complex manual intervention or oversight. The system serves itself by automatically adjusting content based on observed user patterns.
Data Source
AI summary
A method, system and computer program product are provided. For each keyword that is visible on a display device, scanning the content that is stored on a user device, or is accessed from a network connection to identify and extract keywords. Further provided is cross-referencing the extracted keywords with a corpus of scored keywords. Based on the extracted keywords being found in the corpus of scored keywords, expanding and/or abridging any of the extracted keywords based on a score in the corpus of scored keywords prior to displaying the modified content on the display device. wherein the extracted keywords match a keyword in the corpus.