Neuro-Response Priming for Personalized Content Delivery
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Solution Overview
Problem
Conventional content delivery systems rely on demographic and statistical data, leading to semantic, syntactic, metaphorical, and interpretive errors, failing to accurately measure message resonance and priming for products and services, and do not utilize neuro-behavioral and neuro-physiological responses to personalize content effectively.
Innovation Solution
A system that blends neuro-response priming data with user preferences using measurements like EEG, GSR, and EKG to select personalized content by aggregating central nervous system, autonomic nervous system, and effector data, analyzing resonance through event-related potentials and power spectral perturbations, and dynamically inserting content in real-time based on priming levels and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use demographic information and statistical data for content delivery, then content can be delivered in a standardized manner, but semantic, syntactic, metaphorical, cultural, and interpretive errors occur that reduce accuracy
Solution Approach 1:
The patent replaces conventional demographic and statistical data processing with neuro-response measurement systems that directly assess audience resonance and priming. Neuro-response measurements (electrophysiological, behavioral, neurological) substitute for indirect demographic proxies, enabling direct measurement of actual audience response to content and products, thereby eliminating semantic and interpretive errors inherent in conventional approaches.
Solution Approach 2:
The patent fundamentally changes the measurement parameters from demographic attributes (age, gender, income) to neuro-physiological parameters (brain wave patterns, autonomic nervous system responses, effector system responses). This parameter transformation enables direct measurement of resonance and priming states, providing accurate content targeting without the interpretive errors of demographic inference.
2Measurement precision
If neuro-response measurements are implemented, then accurate resonance and priming measurement is achieved, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional integrated system that simultaneously measures multiple types of neuro-response data (central nervous system, autonomic nervous system, and effector system responses) and processes them through unified algorithms. This universal system handles diverse measurement types through common processing architecture, reducing the complexity increase that would result from separate systems for each measurement type.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization modules, resonance calculation algorithms, and priming assessment functions that mediate between raw neuro-response measurements and content delivery decisions. These intermediaries simplify the complex relationship between multiple measurement types and the final content selection, making the overall system more manageable despite the complexity of individual measurement components.
3Adaptability or versatility
If real-time content selection is performed, then personalized content delivery is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary assessment of audience resonance and priming states before final content selection. By pre-processing neuro-response data to identify resonance levels and priming conditions, the system prepares content recommendations in advance, reducing the time required for real-time content selection while maintaining high personalization capability.
Solution Approach 2:
The patent segments the content selection process into distinct stages: neuro-response data acquisition, resonance calculation, priming assessment, and content matching. This segmentation allows parallel processing of different measurement types and enables incremental content selection, reducing overall processing time while maintaining personalized delivery capability.
Data Source
AI summary
A system evaluates source materials such as videos, imagery, web pages, text, etc., in order to determine priming characteristics associated with the source materials. The system also obtains user preferences such as user interests, purchase history, location information, etc. The priming characteristics and user characteristics are blended to obtain blended attributes. The blended attributes are correlated with stimulus material attributes to intelligently and dynamically select stimulus material such as marketing, entertainment, informational materials, etc., for introduction into the source material. The stimulus material may be inserted in real-time or near real-time into the source material for delivery to a user.