User-Specific Summary Generation via Skill-Based Content Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online social networks and collaboration tools lack the ability to determine a user's skill level in relation to specific content and adjust the complexity of messages accordingly, leading to difficulties in understanding and engaging with posts, especially for users with varying levels of expertise.
Innovation Solution
A method and system that analyzes user data to determine skill levels and content complexity, generating user-specific summaries by extracting relevant information and presenting it in a way that bridges the gap between familiar and unfamiliar concepts, using network graphs and natural language processing to create an 'on-ramp' for users to engage intelligently with messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user-specific summaries are generated based on skill level analysis, then user understanding and engagement with content improves, but system complexity and processing requirements increase
Solution Approach 1:
The system segments users into different skill levels (novice, intermediate, expert) and segments content into different complexity levels. This segmentation allows the system to match appropriate content summaries to appropriate user skill levels, improving ease of operation while managing system complexity through structured categorization.
Solution Approach 2:
The system changes the parameter of content presentation by adjusting summary complexity based on user skill level. For novice users, more detailed and explanatory summaries are provided, while expert users receive more concise, technical summaries. This parameter adjustment resolves the contradiction by adapting output complexity to user needs rather than uniformly increasing system complexity for all users.
2Adaptability or versatility
If content complexity is adjusted to match user skill level, then user engagement improves, but information loss may occur in simplified summaries
Solution Approach 1:
The system applies partial action by providing different levels of summary detail appropriate to user skill level. For novice users, more comprehensive summaries are provided to ensure understanding, while expert users receive selective, concise summaries. This partial action approach adapts content delivery without uniformly oversimplifying for all users, thereby reducing information loss while maintaining adaptability.
Solution Approach 2:
The system uses feedback mechanisms to adjust summary complexity based on user interactions and skill level assessments. By monitoring user engagement and understanding, the system can refine its summary generation to maintain information accuracy while adapting to user needs, balancing adaptability with information preservation.
3Measurement precision
If network graphs are used to analyze user relationships and content connections, then personalized summary accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-building and maintaining network graphs of user relationships and content connections during normal system operation. This allows skill level detection and content matching to be performed more quickly during actual user interactions, as the foundational data structure is already in place rather than being constructed in real-time, thus reducing processing time while maintaining detection accuracy.
Data Source
AI summary
The method, computer program product and computer system may include computing device which may collect application data from one or more applications and archive the application data into a datastore. The computing device may generate a network graph based on the archived application data. The computing device may detect a user's focus on a piece of content contained within the one or more applications, retrieve data associated with the piece of content and determine the user's skill in relation to the piece of content. The computing device may determine the complexity of the piece of content in relation to the determined skill of the user and generate and present a summary of the piece of content.