Passive Feedback System for Viewer Engagement Measurement
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Solution Overview
Problem
Television rating systems face challenges in accurately measuring viewer engagement and response due to biases in self-reported data and limitations of existing electronic metering technologies, which fail to capture nuanced viewer interactions.
Innovation Solution
A method and system that passively tracks users' movements, gestures, postures, and facial expressions while viewing multimedia content, generating anonymous feedback to provide more accurate engagement metrics and personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-recorded paper diaries or electronic metering technology are used to measure viewer engagement, then the system can collect basic viewing data, but the measurement precision is low due to response biases and inability to capture nuanced viewer interactions
Solution Approach 1:
The patent replaces manual paper diary recording and simple electronic metering with an automated computer vision system that uses cameras and machine learning algorithms to detect and analyze viewer gestures, postures, and facial expressions. This substitution of mechanical/manual systems with automated optical systems enables precise measurement of nuanced viewer interactions without requiring complex manual data collection infrastructure.
Solution Approach 2:
The patent introduces an intermediary computing system that processes camera footage and translates physical viewer behaviors into engagement metrics. This intermediary layer between the viewer and the rating system enables accurate capture of nuanced interactions while keeping the overall system architecture manageable by separating detection, analysis, and reporting functions.
2Measurement precision
If passive tracking of movements, gestures, postures, and facial expressions is implemented, then measurement precision of viewer engagement is improved, but device complexity increases due to multiple capture devices and processing requirements
Solution Approach 1:
The patent employs a computing device with camera capabilities that serves multiple functions: capturing viewer presence, detecting gestures, analyzing postures, and recognizing facial expressions. By making the computing device universal and multi-functional, the system achieves high measurement precision across multiple engagement dimensions without proportionally increasing device complexity, as one system performs all detection tasks.
Solution Approach 2:
The computing device automatically performs all analysis functions without requiring external specialized equipment or manual intervention. The device self-services by capturing its own footage, processing the visual data through machine learning models, and generating engagement reports, thereby reducing the need for additional complex infrastructure while maintaining high measurement precision.
3Reliability
If anonymous feedback data is aggregated from multiple users, then the reliability of rating data is improved, but loss of information increases due to anonymization and aggregation processes
Solution Approach 1:
The patent extracts only the necessary engagement metrics (gesture frequency, posture duration, facial expression patterns) from the raw camera data while discarding personally identifiable information. This extraction approach enables aggregation of reliable anonymous feedback from multiple users without retaining unnecessary individual details that would increase information loss, focusing only on the behavioral patterns needed for accurate rating.
Data Source
AI summary
A system for generating passive and anonymous feedback of multimedia content viewed by users is disclosed. The multimedia content may include recorded video content, video-on-demand content, television content, television programs, advertisements, commercials, music, movies, video clips, and other on-demand media content. One or more of the users in a field of view of a capture device connected to the computing device are identified. An engagement level of the users to multimedia content being viewed by the users is determined by tracking movements, gestures, postures and facial expressions performed by the users. A report of the response to viewed multimedia content is generated based on the movements, gestures, postures and facial expressions performed by the users. The report is provided to rating agencies, content providers and advertisers. In one embodiment, preview content and personalized content related to the viewed multimedia content is received from the content providers and advertisers based on the report. The preview content and personalized content are displayed to the users.