Brain Type Prediction for Content Segmentation
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Solution Overview
Problem
Organizations face challenges in selecting optimal content offerings for users due to limited demographic and psychographic information, especially for new or anonymous users, as they may be reluctant to provide data or have privacy concerns, and psychographic information can vary over time.
Innovation Solution
A method that predicts a user's brain type by measuring neurophysiological responses to stimuli, such as images, audio, or odors, and classifies them into categories based on similarity, allowing for personalized content selection without requiring extensive user input, using a mapping between segmentation criteria and brain types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If organizations use traditional demographic and psychographic segmentation for content personalization, then they can deliver tailored content to users, but they face limitations when users are new or anonymous and reluctant to provide data
Solution Approach 1:
The patent replaces traditional mechanical data collection methods (surveys, registration forms) with neurophysiological measurement systems. By using brain imaging technologies like fMRI to directly measure neural responses to content stimuli, the system obtains authentic user preferences without requiring voluntary data provision, thus resolving the contradiction between segmentation accuracy and data availability.
Solution Approach 2:
The patent introduces neurophysiological responses as an intermediary between the user and the content delivery system. Instead of directly collecting self-reported demographic or psychographic data, the system uses brain activity patterns as an indirect measure of user preferences and characteristics, enabling accurate segmentation even for anonymous users.
2Adaptability or versatility
If organizations collect extensive user data for personalized content delivery, then they can improve content tailoring, but users may be reluctant to provide data due to privacy concerns
Solution Approach 1:
The patent enables the system to automatically obtain user preference information through passive neurophysiological measurement during natural content consumption. Users simply view or interact with content while their brain responses are measured, eliminating the need for active participation in surveys or data collection processes, thus improving ease of operation while maintaining high adaptability.
Solution Approach 2:
The system replaces manual data provision mechanisms with automated neurophysiological sensing. By measuring brain activity directly through imaging technologies during content interaction, the system obtains comprehensive user preference data without requiring users to consciously provide information, resolving the contradiction between personalization capability and user convenience.
3Measurement precision
If organizations rely on self-reported psychographic information, then they can segment users by attitudes and beliefs, but this information can vary over time and may not reflect true preferences
Solution Approach 1:
The patent replaces self-reporting mechanisms with objective neurophysiological measurement systems. By using brain imaging to directly observe neural responses to content stimuli, the system captures authentic, unconscious preferences that are not subject to the inconsistencies, social desirability biases, or temporal variations inherent in self-reported data, thus improving both accuracy and reliability.
Solution Approach 2:
The patent creates a physiological copy of user preferences through brain activity patterns. Instead of relying on users' subjective reports of their attitudes and beliefs, the system measures the actual neural responses that occur when users engage with content, providing a more reliable and consistent representation of true preferences that cannot be easily altered or misrepresented.
Data Source
AI summary
A method for content delivery includes dividing a reference group of human subjects into multiple segments according to one or more segmentation criteria. Subjective responses of the human subjects to a reference set of data items are collected, and neurophysiological responses of the human subjects to the data items in the reference set are measured. The human subjects are classified into multiple brain types according to the measured neurophysiological responses. Based on the collected subjective responses, a mapping is defined between the segmentation criteria and the brain types and is applied in predicting a brain type of a human subject outside the reference group. A content offering is selected for presentation to the human subject responsively to the predicted brain type.