Small Signal Correlation Engine for Noisy Channel Attribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy approaches fail to provide granular sub-channel attribution for media stimulus in noisy response channels, limiting the ability to identify specific stimuli contributing to user responses, especially in aggregated channels like TV, radio, and print.
Innovation Solution
The implementation of a small signal correlation engine that generates correlation coefficients from stimulus and response data to source-separate multiple contributors, enabling sub-channel attribution and accurate apportionment of event notifications by using machine learning techniques and simulators to model and validate stimulus-response relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If legacy attribution approaches are used at channel level, then computational resources are saved, but measurement precision of stimulus attribution is insufficient
Solution Approach 1:
The patent segments the media channel into multiple sub-channels (e.g., TV stations, radio stations, print publications) and applies correlation analysis at each sub-channel level. This segmentation enables precise attribution of stimulus to specific sub-channels while maintaining computational efficiency through targeted analysis rather than exhaustive processing of all data.
Solution Approach 2:
The patent introduces correlation coefficients as an intermediary metric to measure the relationship between stimulus signals and response signals. These coefficients serve as a mediator that quantifies attribution without requiring complex causal modeling, thus improving measurement precision while avoiding excessive system complexity.
2Measurement precision
If granular sub-channel attribution is implemented, then attribution accuracy is improved, but computational resources increase
Solution Approach 1:
The patent applies correlation analysis selectively to identified sub-channels rather than performing exhaustive analysis on all possible channels and stimuli. This partial action approach achieves sufficient attribution accuracy for the most relevant sub-channels while conserving computational resources by avoiding unnecessary analysis of less relevant channels.
Solution Approach 2:
The patent changes the analysis parameter from broad channel-level aggregation to specific sub-channel-level correlation coefficients. This parameter change enables more accurate attribution by focusing computational resources on measuring correlations at the appropriate level of granularity rather than processing all data at all levels.
3Quantity of substance
If aggregated response channel data is analyzed, then data volume is reduced, but measurement precision of small signal stimulus is lost
Solution Approach 1:
The patent extracts and separates the stimulus signal from the aggregated response channel data by calculating correlation coefficients between known stimulus signals and the response data. This extraction process isolates the small signal stimulus contributions from the noisy aggregated responses, maintaining detection precision while working with the available aggregated data volume.
Solution Approach 2:
The patent replaces traditional signal processing methods with correlation-based analysis to detect small signal stimuli in aggregated data. This substitution uses statistical correlation mathematics instead of complex mechanical or physical signal separation techniques, enabling precise detection of small signals in aggregated response channels with reduced computational requirements.
Data Source
AI summary
A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.