Relational Affect Graphs for Dynamic Group Interaction Analysis
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Solution Overview
Problem
Existing technologies struggle to effectively analyze and represent relational affect in group interactions, as they often require prior knowledge of group members and do not efficiently capture and store interactions across different times and locations.
Innovation Solution
A system and method that utilizes a graph processor to generate a relational affect graph by extracting features from video streams, identifying individuals and storing them as nodes, and interactions as edges, allowing for dynamic and flexible data storage and analysis of group dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video capture devices are used to record group interactions, then interactions can be analyzed in real time or from recorded clips, but the system complexity increases due to the need for processing images and audio from multiple sources
Solution Approach 1:
The system segments the complex analysis task into distinct modules: image processing for visual feature extraction, audio processing for speech and tone analysis, and a graph processing component for relational mapping. Each module handles specific aspects of interaction analysis independently, then integrates results into a comprehensive relational affect graph.
Solution Approach 2:
The patent introduces an intermediary graph representation layer that mediates between raw video/audio data and high-level interaction analysis. The graph structure serves as an intermediate representation that organizes extracted features into relational networks, simplifying the connection between raw data and analytical outputs.
2Measurement precision
If features are extracted from video to determine group affect, then detailed relational data can be captured, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and graph construction during the video processing pipeline, preparing relational affect data in advance for subsequent analysis. By pre-processing video frames to identify individuals, emotions, and interactions before final analysis, the system reduces computation time during the actual analytical phase.
Solution Approach 2:
The graph representation is dynamic and adapts to the video content, with nodes and edges being added or modified based on detected interactions. This dynamic graph structure allows the system to focus computational resources on relevant interactions rather than processing all video data uniformly, optimizing the balance between precision and processing time.
3Adaptability or versatility
If the system stores captured data and processed information in suitable data formats, then analysis by other systems is facilitated, but the device complexity increases due to data format requirements and storage management
Solution Approach 1:
The patent employs a universal graph data structure that serves multiple functions: storing relational affect information, representing interaction patterns, and providing interfaces for various analysis types. This multi-functional graph representation eliminates the need for separate data formats for different analysis purposes, reducing data management complexity while maintaining versatility.
Solution Approach 2:
The system creates standardized graph representations that can be copied and shared between different systems. By exporting relational affect data as standardized graph structures, the system facilitates interoperability without requiring complex data conversion processes, simplifying data management while enhancing adaptability.
Data Source
AI summary
Systems and methods for generating a graph of group affect are provided. The system receives a video of a group of individuals from a video source and extracts a feature that includes data for determining group affect. The system identifies, from the group of individuals, a first individual associated with the feature. The system determines a first node, in a graph, that is associated with the first individual and stores the feature associatively with the first node when the feature is associated only with the first individual. The system identifies a second individual associated with first individual and the feature, and determines second node, in the graph, that is associated with the second individual. The system generates an edge in the graph associated with the first node and the second node, and stores the feature associatively with the edge.