Attentiveness Metrics via Physiological Sensor Fusion
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Solution Overview
Problem
Current methods for evaluating media content performance, such as advertising, rely on active user feedback that only captures conscious emotional responses, failing to accurately predict performance due to their reliance on rationalized thoughts rather than passive emotional states, and lack metrics to track viewer attentiveness effectively.
Innovation Solution
A system and method for collecting and analyzing data from client devices using AI-driven modules to generate attentiveness metrics by combining behavioral, physiological, and self-reported data, synchronizing these metrics with contextual attributes to provide insights into user attention and emotional states, enabling targeted content optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active user feedback (self-reported feedback) is used to evaluate media content performance, then users can provide conscious emotional responses through questionnaires or speech, but the feedback only captures rationalized thoughts rather than true passive emotional states, leading to inaccurate performance prediction
Solution Approach 1:
The patent introduces physiological sensors (heart rate monitor, galvanic skin response sensor, temperature sensor, accelerometer, gyroscope) as intermediaries to measure emotional states indirectly. These sensors detect physiological changes that occur during emotional experiences, providing an objective measure that bypasses the need for users to consciously report their feelings, thus capturing true emotional states rather than rationalized responses
Solution Approach 2:
The patent replaces the mechanical system of active user feedback (questionnaires, speech recording) with a physiological measurement system. Instead of relying on users to articulate their emotional states, the system uses biosensors to detect physiological markers of emotion, substituting subjective self-reporting with objective physiological data collection
2Measurement precision
If passive physiological parameters are collected to measure true emotional state, then accurate emotional state data can be obtained, but the system complexity increases due to multiple sensors and data processing requirements
Solution Approach 1:
The patent implements a multi-functional data collection system where a single integrated framework collects multiple types of physiological data (heart rate, skin conductance, temperature, motion) simultaneously. The system processes diverse data types through a unified machine learning pipeline that can handle different sensor inputs, making the complex system manageable and scalable while maintaining measurement accuracy across multiple emotional dimensions
Solution Approach 2:
The patent combines multiple physiological measurement functions into a single integrated system. Rather than using separate systems for heart rate monitoring, skin conductance measurement, and motion tracking, the patent merges these functions into one cohesive data collection framework that processes all physiological inputs together, reducing overall system complexity while maintaining comprehensive emotional state assessment
3Loss of information
If behavioral data (facial responses, gestures) is collected to track user attentiveness, then viewer attention metrics can be obtained, but the difficulty of detecting and measuring true attention increases due to the need for multiple data sources and synchronization
Solution Approach 1:
The patent segments the attentiveness measurement task into distinct components: facial response analysis, gesture detection, and physiological state monitoring. Each component is handled by specialized processing modules that analyze specific types of data independently, then the results are integrated to form a comprehensive attentiveness assessment. This segmentation makes the complex measurement task more manageable and accurate
4Reliability
If multiple data sources (behavioral, physiological, self-reported) are combined to obtain emotional state information, then more accurate and reliable emotional data can be obtained, but the quantity and complexity of data to be processed increases significantly
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors multiple data sources and adjusts its processing based on the quality and consistency of incoming data. The machine learning models receive feedback from physiological sensors, behavioral observations, and self-reported feedback, dynamically weighting and integrating these sources to produce reliable emotional state assessments while managing data processing efficiency through adaptive filtering and prioritization
Data Source
AI summary
A system and method for rapidly and scalably tracking attentiveness. The system includes means for collecting relevant data streams from a plurality of client devices while consumers view content, means for analysing the collected data with an AI-driven module that outputs one or more attentiveness metrics indicative of real attention, and means for synchronising the collected data with the attentiveness metrics. The system provides the ability to synchronise attentiveness metrics with other data streams to make accessible the reasons that drive attention. A digital advertising campaign can be optimised using an effectiveness data set that expresses evolution over time of an attentiveness parameter. An effect on the attentiveness parameter caused by an adjustment to a target audience can be predicted and evaluated against a campaign objective, which can be updated for predictions that yield a positive effect.