Video Relationship Graphs for Real-Time Key Feature Detection
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Solution Overview
Problem
Evaluating the strength of relationships between individuals is difficult, especially in real-time, particularly for those with impairments affecting verbal and non-verbal communication abilities, and existing methods lack the granularity to identify key features associated with positive outcomes.
Innovation Solution
A system and method that utilizes digital video data to create relationship graphs by extracting image, audio, and semantic text features, analyzing them with machine learning models, and identifying key features predictive of positive outcomes through trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated analysis of video data is implemented to evaluate relationships, then measurement precision and objectivity are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the relationship evaluation process into distinct modules: video data acquisition, feature extraction (visual, audio, semantic), relationship graph construction, and key feature detection. Each module processes specific aspects of the interaction independently, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces relationship graphs as an intermediary data structure that transforms complex video interaction data into a structured format representing interpersonal relationships. This intermediary representation enables precise measurement of relationship strength and dynamics without requiring direct complex analysis of raw video data.
2Speed
If real-time relationship evaluation is implemented, then responsiveness and timeliness are improved, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary processing of video data by extracting key features (visual, audio, semantic) and constructing relationship graphs in real-time as interactions occur. This preliminary action enables rapid evaluation without requiring intensive post-processing, balancing speed with computational efficiency.
Solution Approach 2:
The patent extracts only the most relevant features from video data (visual cues, audio characteristics, semantic content) rather than processing all raw data. This selective extraction reduces computational energy consumption while maintaining real-time evaluation capability.
3Measurement precision
If detailed feature extraction is performed to identify key features, then measurement precision is improved, but loss of time and processing complexity increase
Solution Approach 1:
The patent replaces manual feature analysis with automated machine learning models that detect visual, audio, and semantic features. This substitution maintains high measurement precision for key feature identification while significantly reducing processing time compared to human analysis.
Solution Approach 2:
The system dynamically adjusts the depth of feature extraction based on the interaction context and evaluation needs. By changing parameters such as feature extraction granularity and analysis depth, the system optimizes the balance between measurement precision and processing time for different scenarios.
Data Source
AI summary
A method includes acquiring digital video data that portrays an interacting event, extracting image data, audio data, and semantic text data from the video data, analyzing the extracted data to identify a plurality of video features, and analyzing the plurality of video features to create a relationship graph. The interacting event comprises a plurality of interactions between plurality of individuals and the relationship graph comprises a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents the plurality of interactions. The method further comprises determining whether a first key feature is present in the relationship graph, wherein presence of the first key feature is predictive of a positive outcome of the interacting event.