Recurrent Neural Network for Incremental Event Causality Analysis
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Solution Overview
Problem
Existing technologies face challenges in accurately determining the incremental impact of specific events in a sequence on a target outcome, particularly in complex environments like electronic marketplaces, due to the interdependence and sequential nature of events, making it difficult to isolate the causal contribution of individual events to desired actions.
Innovation Solution
The use of machine learning sequence analysis systems, specifically implementing recurrent neural networks (RNNs) like Long Short-Term Memory (LSTM) networks, to featurize event sequences and compare factual and counter-factual scenarios, enabling the isolation of incremental impact by quantifying the difference in target outcome probabilities when an event occurs or does not occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analytical methods are used to determine event impact, then the analysis process is simple, but the measurement precision of incremental impact is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/statistical analytical methods with a machine learning-based causal analysis system. The system uses trained models to automatically process event sequences and determine incremental impact, substituting manual or simple statistical analysis with an intelligent system that handles the complexity of causal inference in sequential data.
2Measurement precision
If machine learning sequence analysis is implemented to accurately measure incremental impact, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on historical event sequence data before deployment. The model is trained offline to learn causal patterns and relationships, so that when deployed, it can directly infer incremental impact without requiring complex real-time computations. This preliminary training phase separates the complexity of model development from the simplicity of inference execution.
3Measurement precision
If detailed event sequence analysis is performed to isolate causal contribution, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming manual or step-by-step analytical methods with a pre-trained machine learning model that performs causal analysis in a single computational pass. The model has already learned to identify causal patterns during training, allowing it to quickly infer incremental impact of individual events in new sequences without requiring sequential manual analysis of each event's contribution.
Data Source
AI summary
Aspects of the present disclosure relate to machine learning techniques for identifying the incremental impact of different past events on the likelihood that a target outcome will occur. The technology can use a recurrent neural network to analyze two different representations of an event sequence—one in which some particular event occurs, and another in which that particular event does not occur. The incremental impact of that particular event can be determined based on the calculated difference between the probabilities of the target outcome occurring after these two sequences.