Video Analytics System for Predicting User Behavior
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Solution Overview
Problem
Current methods fail to effectively utilize the rich information present in video communications, such as user presentation, mannerisms, and background, to predict user behavior in real-time video communication services, limiting their potential for personalized interactions and outcomes.
Innovation Solution
A video analytics system that analyzes video communications to extract time-coded video behavioral data, determines emotional states, and applies linguistic-based algorithms to identify personality types, integrating this information into predictive models to forecast communication outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video communications are analyzed to extract behavioral data and determine emotional states, then prediction accuracy of user behavior is improved, but system complexity increases
Solution Approach 1:
The system segments video communication analysis into distinct modules: video component analysis for behavioral data, audio component analysis for emotional states, and integration with predictive models. This modular approach improves measurement precision while managing system complexity through organized functional breakdown.
Solution Approach 2:
The predictive model serves multiple functions by integrating diverse data sources (video behavioral data, audio emotional analysis, biographical information) into a unified prediction framework. This multi-functionality approach handles complex analysis tasks through a single integrated system rather than separate specialized systems.
2Measurement precision
If multiple data sources including video behavioral data and audio emotional analysis are integrated, then prediction accuracy is improved, but information processing time increases
Solution Approach 1:
The system performs preliminary analysis by extracting video behavioral data and audio emotional analysis in parallel before integrating them into the predictive model. This preliminary processing of multiple data sources prepares information in advance, reducing the time required for final prediction while maintaining high accuracy.
Solution Approach 2:
The predictive model continuously processes integrated data from video and audio components without interruption, maintaining a steady flow of analysis. This continuous processing approach ensures that all data sources are utilized efficiently, improving prediction accuracy while minimizing idle processing time.
3Measurement precision
If linguistic-based algorithms are applied to determine personality types, then user profiling accuracy is improved, but computational requirements increase
Solution Approach 1:
The system extracts only the most relevant linguistic features from audio components for personality type determination, rather than processing complete speech content. This selective extraction approach improves user profiling accuracy by focusing on key indicators while reducing overall computational requirements.
Solution Approach 2:
The linguistic-based algorithm processes speech data by transforming it into standardized personality type parameters rather than maintaining raw computational complexity. This parameter transformation reduces computational requirements while preserving the accuracy needed for effective user profiling.
Data Source
AI summary
Methods and systems to predict user behavior based on analysis of a video communication by one or more processors, which methods include receiving a user video communication, extracting video analysis data optionally including facial analysis data for the user from the video communication, extracting, by the one or more processors, voice analysis data from the user video communication, generating an outcome prediction score based on the video analysis data and voice analysis data that predicts a likelihood that a user will take an action leading to an outcome.