Contextualized Video Retrieval for Enterprise Networks
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Solution Overview
Problem
Existing multimedia processing systems for enterprise networks are inefficient due to the overwhelming amount of domain knowledge provided via tutorial and seminar recordings, which require users to manually review numerous videos to find relevant information for task completion. These systems lack interactive learning capabilities and fail to customize multimedia data sets based on user persona and context.
Innovation Solution
A graph-based semantic contextualization service that generates distilled multimedia data sets by extracting relevant slices from multimedia data based on user context, using a multi-modal knowledge graph and graph neural networks to indicate relationships among multimedia slices, and providing an interactive learning approach tailored to user persona and tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually review numerous videos to find relevant information, then they can access domain knowledge, but the time required increases significantly
Solution Approach 1:
The system extracts only the relevant portions of multimedia content based on user context, persona, and task requirements. Instead of presenting entire videos, the system identifies and extracts specific slices containing the needed information, thereby reducing review time while maintaining access to domain knowledge.
Solution Approach 2:
The multimedia content is segmented into discrete slices that can be independently evaluated and selected. The system divides large video datasets into manageable segments, allowing for efficient filtering and presentation of only those segments relevant to the user's specific needs.
2Loss of information
If comprehensive multimedia data sets are provided, then users have access to all domain knowledge, but the data becomes overwhelming and difficult to navigate
Solution Approach 1:
The system applies different levels of filtering and customization to different users based on their specific contexts, personas, and tasks. Instead of uniform treatment, each user receives a tailored subset of multimedia content that matches their local needs and requirements.
Solution Approach 2:
The system extracts and presents only the specific portions of multimedia content relevant to each user's context, removing unnecessary information while preserving completeness of needed knowledge.
3Device complexity
If generic multimedia data sets are provided, then the system is simple to implement, but it lacks customization for different user personas and contexts
Solution Approach 1:
The system dynamically adapts the multimedia content selection based on user persona, context, and task requirements. The filtering and recommendation mechanisms adjust in real-time according to user-specific parameters, enabling customization without requiring separate static configurations for each user type.
Solution Approach 2:
The system changes multiple parameters simultaneously (user persona, task type, context information) to generate customized multimedia data sets. By varying these parameters, the system achieves high adaptability while maintaining a unified implementation framework.
4Productivity
If interactive learning capabilities are added, then learning effectiveness improves, but system complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with multimedia content inform subsequent recommendations and refinements. This feedback loop enables interactive learning by continuously adapting to user needs and preferences.
Solution Approach 2:
The system performs preliminary analysis of user persona, context, and task requirements before presenting multimedia content. This preliminary action prepares customized data sets in advance, making the interactive learning process more efficient without adding significant complexity during actual user interaction.
Data Source
AI summary
Methods are provided for generating distilled multimedia data sets tailored to user's persona and/or task(s) to be performed associated with an enterprise network and enable interactive contextual learning using a multi-modal knowledge graph. Methods involve obtaining multimedia data from one or more data sources related to operation or configuration of an enterprise network and determining context for generating a distilled multimedia data set based on at least one of user input and user persona. The methods further involve generating, based on the context, the distilled multimedia data set that includes a set of multimedia slices generated from the multimedia data using a multi-modal knowledge graph. The multi-modal knowledge graph is generated using a graph neural network and indicates relationships among a plurality of slices of the multimedia data. The methods further involve providing the distilled multimedia data set for performing one or more actions associated with the enterprise network.