Media Performance Prediction via Facial Expression Analysis
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Solution Overview
Problem
Conventional methods for evaluating advertising performance are inaccurate and inconsistent across different product categories, and face challenges in scaling to large sample sizes due to reliance on active feedback and passive techniques that fail to capture true emotional states of consumers.
Innovation Solution
A computer-implemented method that collects raw input data from users, including behavioral and physiological data, processes it to extract time series of descriptor and emotional state data points, and uses classification models to predict performance data, such as sales lift, by analyzing dynamic changes in user responses to media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active feedback is used to measure user emotional state, then user awareness and rationalized feedback are obtained, but scalability to large sample sizes is limited and real-time emotional state capture is impossible
Solution Approach 1:
The patent replaces active self-reporting mechanisms (questionnaires, verbal feedback) with passive physiological sensing systems. Sensors continuously collect behavioral and physiological data (facial expressions, heart rate, galvanic skin response) without requiring user intervention, enabling both high measurement precision and scalability to large sample sizes simultaneously
Solution Approach 2:
The system enables users to provide emotional state data automatically through their own physiological responses. The sensors detect and record emotional states passively as users naturally interact with media content, eliminating the need for users to consciously report their emotions while maintaining measurement accuracy
2Quantity of substance
If retrospective correlation methods are used to evaluate advertising performance, then sales performance data can be obtained, but emotional state of consumers remains blind and real-time feedback is unavailable
Solution Approach 1:
The system performs preliminary measurement of emotional state during media consumption before final performance evaluation. By continuously monitoring physiological and behavioral data in real-time during ad exposure, the system captures emotional responses that can then be correlated with subsequent sales performance, preventing loss of emotional state information
Solution Approach 2:
The system implements continuous feedback loops where physiological and behavioral data are constantly monitored and fed back to the analysis system. This real-time feedback mechanism allows for dynamic tracking of emotional state changes during media consumption, enabling both emotional state measurement and performance correlation
3Productivity
If passive emotional state measurement is used, then scalability is improved, but accuracy and consistency across different product categories deteriorates
Solution Approach 1:
The patent implements a universal measurement system that collects multiple types of behavioral and physiological data (facial expressions, head pose, heart rate, galvanic skin response) that can be applied across different product categories. This multi-functional approach ensures measurement accuracy is maintained whether measuring emotional response to food advertisements, technology products, or other categories
Solution Approach 2:
The system combines multiple complementary measurement modalities (facial expression analysis, physiological sensors, behavioral tracking) into a composite measurement approach. This composite method compensates for limitations of individual measures and maintains high accuracy across diverse product categories while preserving scalability
Data Source
AI summary
Methods and systems of predicting performance data for a piece of media content that is consumable by a user at a client device are provided. In one or more embodiments, the method collects raw input data, such as from a webcam, indicative of a user's response to the media content as the user watches the content. The data is processed to extract and obtain a series of head pose signals and facial expression signals, which is then input to a classification model. The model maps the performance data of the media content over time in response to the signals evaluated by the method to produce a prediction of the performance of the piece of media content.