Hidden Evidence Causation Linking via Forecasting
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Solution Overview
Problem
Conventional techniques fail to identify the causation of hidden evidence, leading to a lack of explanation behind retrieved evidence and incomplete understanding of event correlations.
Innovation Solution
A system and method for computing correlation and causation links based on hidden evidence, using devices such as a correlation computing device, forecasting device, and causation computing device to analyze user inputs and forecast future correlations and causations, with filtering mechanisms to minimize false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional techniques are used to identify patterns and correlations in data, then predictive awareness and likelihood analysis can be provided, but the causation of events cannot be identified
Solution Approach 1:
The system segments the analysis into distinct functional modules: a correlation computing device for computing correlation links, a forecasting device for forecasting hidden evidence, and a causation computing device for computing causation links. This segmentation allows each module to specialize in a specific aspect of the analysis while working together to provide comprehensive causation identification without overwhelming system complexity
Solution Approach 2:
The forecasting device acts as an intermediary between the correlation computing device and the causation computing device. It takes correlation links as input and generates forecasted hidden evidence that serves as input for causation analysis, bridging the gap between pattern recognition and causation identification
2Measurement precision
If conventional retrieval techniques are used to find hidden evidence, then matching scores can be generated, but explanation behind the evidence is lacking
Solution Approach 1:
The system performs preliminary forecasting of hidden evidence using the forecasting device before conducting the final causation analysis. By pre-computing the forecasted hidden evidence from correlation links, the system prepares the data in advance, enabling faster and more precise causation computation without sacrificing explanation quality
Solution Approach 2:
The system implements a feedback loop where correlation links are computed from hidden evidence, forecasted to generate future evidence predictions, and then fed back into the causation computing device. This iterative feedback process refines the causation analysis by continuously improving the quality of evidence explanations through multiple passes of computation and validation
Data Source
AI summary
A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including a forecasting device configured to forecast hidden evidence found in relation to a user input in hidden cycle measurements into future forecasted cycle measurements, where the forecasted hidden cycles are transformed into an amplitude versus frequency histogram with each histogram being compared to each other histogram and determined if causation is a candidate, and if causation is a candidate, a probability density function is applied to produce a degree of causation of a causation link for the hidden evidence in relation to the user input.