Brain Feature Activity Map Database for Content Characterization
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Solution Overview
Problem
Existing techniques for evaluating content on social media platforms are manual and slow, making it challenging to efficiently identify and remove objectionable content.
Innovation Solution
A computer-implemented method utilizing a brain feature activity map database to characterize content by supplying stimuli to an organism, measuring brain activity, and creating a brain feature activity map, which is then used to categorize new stimuli based on similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation techniques are used to identify objectionable content, then evaluation accuracy can be maintained through human judgment, but the process becomes slow and cumbersome
Solution Approach 1:
The patent replaces manual human evaluation (mechanical system) with an automated computational system that uses machine learning models and brain activity data to evaluate content. This substitution enables rapid automated classification of content while maintaining evaluation accuracy through sophisticated algorithms that analyze brain feature activity maps and compare them against databases of known content types.
2Productivity
If automated evaluation systems are implemented, then content classification speed increases, but system complexity increases
Solution Approach 1:
The patent segments the automated evaluation system into distinct modular components: a brain activity measurement module that collects neural data, a feature extraction module that identifies relevant patterns, a machine learning classification module that categorizes content, and a database module that stores brain feature activity maps. This segmentation manages system complexity by organizing functions into separate, manageable units that can be developed and maintained independently while working together to achieve rapid content classification.
3Extent of automation
If brain activity measurement is used to characterize content, then automated content classification becomes possible, but measurement and analysis complexity increases
Solution Approach 1:
The patent introduces brain feature activity maps as an intermediary representation between raw brain activity measurements and content classification decisions. The system measures brain activity in response to content stimuli, transforms this complex neural data into standardized activity maps that highlight relevant features, and then uses these maps as input for classification. This intermediary step simplifies the measurement and analysis process by converting complex brain activity data into a more manageable format that can be systematically compared across different content types.
Data Source
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AI summary
A computer implemented method includes supplying stimuli to an organism, measuring brain activity of the organism responsive to the stimuli and producing a brain feature activity map characterizing the brain activity. The operations of supplying, measuring and producing are repeated for different stimuli to form a brain feature activity map database. New stimuli are received. New stimuli features are mapped to a projected brain activity map. The projected brain activity map is compared to the brain feature activity map database to identify similarities and dissimilarities between the projected brain activity map and entries in the brain feature activity map database to designate a match. The new stimuli are characterized based upon the match.