Gesture Detection System for Sentiment Analysis
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Solution Overview
Problem
Current technologies lack effective methods for detecting and analyzing user gestures in response to content displayed in a physical environment, such as television programs or movies, to determine user sentiment and reactions, which limits personalized content delivery and advertising targeting.
Innovation Solution
A system that uses a combination of video cameras, facial recognition, and machine learning algorithms to detect and analyze user gestures, including hand and facial movements, to determine user sentiment and emotions, and adjusts content presentation accordingly, including selecting targeted advertisements based on user reactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video cameras and facial recognition are used to detect user gestures, then user sentiment analysis capability is improved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional architecture where a single camera system serves multiple purposes: capturing video feeds for gesture detection, extracting facial features for recognition, and analyzing body movements. This universal approach allows the same hardware infrastructure to support diverse analysis functions (gesture recognition, facial expression analysis, sentiment determination), thereby improving measurement precision without proportionally increasing device complexity
Solution Approach 2:
The patent introduces intermediate processing layers including gesture detection modules, facial recognition algorithms, and sentiment analysis engines that act as mediators between the raw video input and the final content delivery decisions. These intermediary components break down the complex task of sentiment analysis into manageable sub-tasks, each handled by specialized modules, thus managing system complexity while achieving high detection accuracy
2Adaptability or versatility
If real-time gesture analysis is implemented, then personalized content delivery is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video feeds to extract key features (facial landmarks, gesture contours, body pose) before full sentiment analysis is required. Facial recognition models are pre-trained and stored, allowing rapid matching during real-time operation. This preliminary feature extraction and model preparation enables the system to deliver personalized content quickly without performing complete analysis from scratch each time
Solution Approach 2:
The patent implements partial action by analyzing only the most relevant gestures and facial expressions that indicate sentiment changes, rather than processing every movement. The system focuses on key indicator gestures (e.g., smiling, frowning, specific hand movements) and uses threshold-based filtering to ignore minor movements that don't convey sentiment information, thereby reducing processing time while maintaining effective personalized content delivery
3Measurement precision
If multiple user gestures are tracked simultaneously, then user engagement measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the analysis of multiple users by creating separate processing pipelines for each detected user. Each user's video feed is independently processed to track their gestures, facial expressions, and engagement metrics. This segmentation allows the system to maintain high measurement precision for each individual user while managing data processing complexity through modular, independent analysis streams rather than attempting to process all users simultaneously in a single complex pipeline
Solution Approach 2:
The patent applies local quality by assigning different analysis depths to different users based on their engagement levels and relevance to the content. Users showing active engagement (multiple gestures, sustained attention) receive more detailed analysis with higher computational resources, while passive users receive lighter processing. This differentiated approach improves overall measurement accuracy for engaged users without proportionally increasing total data processing complexity
Data Source
AI summary
Techniques for identifying content displayed by a content presentation system associated with a physical environment, detecting a gesture of a user located within the physical environment, and storing information associated with the gesture in relation to the displayed content are disclosed.