Key Frame Analysis for Remote Collaboration Session Indexing
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Solution Overview
Problem
Existing systems for remote collaborative meetings struggle with real-time retrieval of relevant content due to large data volumes and inability to effectively index user interactions, particularly in screen-sharing sessions, which limits the ability to view and search shared content efficiently.
Innovation Solution
The system employs image-based key frame analysis to detect, classify, and index user interactions such as cursor movements and text editing during remote collaboration sessions, enhancing key frames for visual representation and aggregation of interactions for targeted searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text editing and cursor motion detection is performed on live document streams, then user interaction retrieval capability is improved, but processing time and system complexity increase due to large data volumes
Solution Approach 1:
The patent segments the continuous document stream into discrete key frames at regular intervals. This segmentation transforms the continuous data stream into manageable discrete units that can be processed individually, reducing processing time while maintaining comprehensive coverage of user interactions throughout the document stream.
Solution Approach 2:
The patent performs preliminary detection and classification of user interactions (cursor movements, text selections, text edits) on key frames before final indexing and retrieval operations. This preliminary action filters and pre-processes the data, reducing the volume of information that requires full processing and thereby decreasing overall processing time.
2Loss of information
If every frame is indexed using optical character recognition, then search completeness is improved, but processing speed decreases due to large data volume
Solution Approach 1:
Instead of processing every frame continuously, the patent segments the document stream into key frames at regular intervals. This segmentation maintains search completeness by capturing representative moments while dramatically reducing the total processing volume, thereby improving processing speed.
Solution Approach 2:
The patent applies partial action by indexing only key frames rather than every single frame. This partial approach provides sufficient search capability without the excessive processing cost of analyzing all frames, achieving an optimal balance between search completeness and processing speed.
3Measurement precision
If user interactions are detected and classified in real-time, then content retrieval relevance is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex real-time processing task into distinct sequential stages: key frame extraction, interaction detection, interaction classification, and indexing. This segmentation reduces system complexity by breaking down the complex process into manageable modules that can be implemented and maintained more easily.
Solution Approach 2:
The patent applies different processing qualities to different types of interactions. By classifying interactions into categories (cursor movements, text selections, text edits), the system can apply appropriate detection and indexing methods specific to each interaction type, improving retrieval relevance while managing complexity through specialized processing rather than uniform complex processing.
Data Source
AI summary
Systems and methods of performing a process are provided, including receiving a live document video stream of a remote collaboration session, detecting a cursor action in the live document video stream, classifying the detected cursor action into an action category, detecting key frames of the live document video stream, indexing the detected key frames based on the action category, detecting a keyword in the indexed key frames, indexing the key frames using the category, visualizing the cursor action in the key frames based on the action category, and displaying the visualized cursor action.