Context-Aware Cognitive Processing for Video Learning
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Solution Overview
Problem
Current query processing techniques fail to provide context-aware responses to user queries during dynamic cognitive interactions with pre-processed data, especially in one-directional information flows like video lectures, lacking the ability to incorporate real-time context and user knowledge states.
Innovation Solution
A computer-implemented method that creates a progressively increasing map of knowledge states by processing text, audio, and video inputs to analyze user queries and provide customized responses, leveraging a knowledge base to incorporate context information and user interactions for personalized learning experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an independent search engine is used to answer user questions, then the response can be provided regardless of video content, but the response does not incorporate the context associated with the processing of the presented content
Solution Approach 1:
The patent merges the search engine functionality with the video content context by integrating the video processor, knowledge base, and search engine into a unified system. The system combines general knowledge search results with context-specific information from the video lecture, ensuring responses are both fast and contextually accurate.
Solution Approach 2:
The patent introduces a context-aware processing intermediary that sits between the user query and the search engine. This intermediary analyzes the video content context, user knowledge state, and query intent before retrieving search results, then synthesizes them into contextually relevant responses.
2Adaptability or versatility
If a human teacher is available to respond to user questions during video lectures, then context-aware responses can be provided, but the cost becomes prohibitive for scaling to large numbers of users
Solution Approach 1:
The patent creates a digital copy of the teacher's function through an automated system that processes video content, maintains knowledge bases, and generates context-aware responses. This virtual teacher copy can serve unlimited users simultaneously without the scaling costs associated with human instructors.
Solution Approach 2:
The system enables self-service by automatically analyzing video content, building knowledge representations, and generating responses to user queries without human intervention. The automated knowledge base and processing algorithms handle context-awareness independently, eliminating the need for human teachers while maintaining response quality.
3Adaptability or versatility
If context-aware cognitive processing is implemented to provide personalized responses, then user engagement and learning experience are enhanced, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing video content, extracting key concepts, and building knowledge bases before users arrive. This advance preparation stores structured information and context representations that can be quickly retrieved and applied during user interactions, reducing real-time processing complexity.
Solution Approach 2:
The patent segments the complex processing task into distinct modules: video content analysis, knowledge base construction, user knowledge state tracking, query processing, and response generation. Each module handles a specific aspect of context-awareness independently, making the overall system more manageable and scalable despite the high level of personalization.
Data Source
AI summary
Methods, systems, and computer program products for context-aware cognitive processing are provided herein. A method includes creating a progressively increasing map of a knowledge state as a function of time based on one or more topics covered during a user session by utilizing one or more processing techniques to process at least one of (i) text input, (ii) audio input and (iii) video input derived from content of the user session; analyzing a knowledge base to determine a response to a user query submitted during the user session; and customizing the response to the user based on (i) the map of the knowledge state and (ii) a collection of one or more items of context information pertaining to the user.