Neurophysiological Measurement System for Marketing Effectiveness
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Solution Overview
Problem
Conventional systems for measuring the effectiveness of entertainment and marketing are inefficient and inaccurate due to semantic, syntactic, metaphorical, cultural, social, and interpretative errors and biases, and are limited by the use of isolated neurophysiological measurements.
Innovation Solution
A system that combines central nervous system, autonomic nervous system, and effector measurements using multiple modalities such as EEG, EOG, GSR, and others, with intra-modality and cross-modality response synthesis to provide a more accurate assessment of marketing and entertainment effectiveness by analyzing neural interactions and frequency bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional survey-based evaluation systems are used, then implementation is simple and cost-effective, but measurement accuracy and reliability are poor due to semantic, syntactic, metaphorical, cultural, social, and interpretative errors and biases
Solution Approach 1:
The patent combines multiple neurophysiological measurement modalities (EEG, EOG, GSR, EMG, fMRI, PET, SPECT) into a single integrated system. This merging of previously isolated measurement approaches enables cross-modality response synthesis, which resolves the technical contradiction by achieving high measurement accuracy through multi-modal data integration while managing system complexity through unified data processing architecture.
Solution Approach 2:
The patent creates a composite measurement system that integrates data from different neurophysiological modalities. By treating the combination of EEG, EOG, GSR, and other measurements as a composite information structure, the system achieves enhanced measurement precision through cross-modality synthesis while maintaining manageable complexity through standardized processing protocols.
2Measurement precision
If isolated neurophysiological measurements are used, then device complexity is low, but measurement precision and reliability are limited due to inability to capture comprehensive neural responses
Solution Approach 1:
The patent creates a universal measurement system that can simultaneously perform multiple neurophysiological measurements (central nervous system via EEG, autonomic nervous system via GSR, effector responses via EMG). This multi-functional approach resolves the contradiction by achieving comprehensive measurement reliability while managing complexity through integrated data processing and cross-modality response synthesis.
Solution Approach 2:
The patent merges previously isolated neurophysiological measurement modalities into a unified system. By combining CNS measurements (EEG), ANS measurements (GSR), and effector measurements (EMG) with cross-modality synthesis, the system achieves high measurement reliability while controlling complexity through standardized processing architecture.
3Reliability
If multiple modalities of neurophysiological measurements are combined, then measurement accuracy and reliability improve through cross-modality response synthesis, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces cross-modality response synthesis as an intermediary processing layer that integrates data from multiple neurophysiological modalities. This intermediary function resolves the technical contradiction by enabling reliable multi-modal integration while managing system complexity through standardized synthesis protocols and unified data processing architecture.
4Loss of information
If survey-based methods are used, then ease of operation is high, but loss of information occurs due to semantic, syntactic, metaphorical, cultural, social, and interpretative errors and biases
Solution Approach 1:
The patent replaces survey-based mechanical questioning systems with direct neurophysiological measurement systems. By substituting self-reported survey data with objective neural measurements (EEG, EOG, GSR, EMG), the system eliminates semantic, syntactic, and interpretative errors while maintaining operational simplicity through automated data collection and processing.
Data Source
AI summary
An example system includes an analyzer to identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via a first modality of collection from a subject while the subject is exposed to media, and modify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via a second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern. The example system includes an estimator to determine an effectiveness of the media based on the modified degree of amplitude synchrony.