Machine Learning Estimation for Downstream Impact
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Solution Overview
Problem
Existing systems lack the ability to programmatically identify actions with high downstream impact (DSI) due to the complexity of causality, leading to manual identification and limited discovery of high-value actions within historical event data.
Innovation Solution
A multi-stage machine learning model is employed to analyze sequences of event data using factual and counterfactual modeling, enabling the identification of incremental impacts of actions and predicting new actions with potential high DSIs by encoding actions as machine-readable representations and utilizing recurrent neural networks for correlation and generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification methods are used to find high DSI actions, then simplicity and ease of implementation are maintained, but productivity and the ability to discover high-value actions are severely limited
Solution Approach 1:
The patent replaces manual identification processes with an automated machine learning system that uses factual and counterfactual modeling. The system automatically analyzes event sequences to identify actions with high downstream impact, substituting human manual review with computational algorithms that can process large volumes of data efficiently.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw event data and actionable insights. This intermediary system processes complex causal relationships and translates them into identified high-value actions, bridging the gap between raw data and decision-making without requiring direct human analysis of every event sequence.
2Measurement precision
If comprehensive analysis of all actions is performed to ensure high measurement precision, then identification accuracy improves, but loss of time and computational resources increases significantly
Solution Approach 1:
The patent performs preliminary processing by encoding actions as machine-readable representations and pre-processing event sequences before detailed analysis. This preliminary encoding structure enables faster subsequent analysis while maintaining measurement precision, as the data is organized in an optimal format for the machine learning model to process efficiently.
Solution Approach 2:
The patent uses counterfactual modeling to analyze not just what actually happened but what could have happened under different conditions. This partial analysis of alternative scenarios allows the system to identify high-impact actions without needing to exhaustively analyze every possible action sequence, reducing time loss while maintaining identification accuracy.
Data Source
AI summary
Systems and techniques are disclosed for machine learning based improvements in estimation techniques. An example method includes obtaining a seed specifying a portion of an action for evaluation with respect to a networked computing environment. A description of the action is completed by providing a first recurrent neural network (RNN) with the seed to generate one or more additional words, with the action represented by the description not being enabled yet in the networked computing environment. An estimated down-stream impact (DSI) associated with the action is determined based on a second RNN, with the estimated DSI indicating an estimated measure of impact to the networked computing environment which would be caused by performance of the example action after enabling the example action. Output of an interactive user interface including information representing the action and the estimated DSI is caused.