Causality Reasoning Lattice for Automated Event Analysis
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Solution Overview
Problem
Determining causal relations becomes increasingly challenging as the amount of data collected grows, requiring resource-intensive manual analysis to accurately detect relationships between causes and effects.
Innovation Solution
A computer-implemented method for causality reasoning that involves determining a lattice with elements containing pairs of cause events and effect events, extending the lattice to include additional events, and identifying corresponding effect events based on the cause events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to detect causal relationships, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent introduces an intermediary computational system that bridges manual analysis and automated processing. The system uses trained models to detect causal relationships automatically, serving as a mediator that preserves the accuracy benefits of manual analysis while achieving the throughput of automated systems. This intermediary approach resolves the contradiction by translating human expertise into machine-executable algorithms.
Solution Approach 2:
The patent applies preliminary action by pre-training models on labeled data containing known causal relationships before deploying them for automated detection. This preliminary training phase captures human expertise in advance, allowing the system to subsequently perform causal relationship detection automatically without requiring real-time manual intervention, thus maintaining high accuracy while improving productivity.
2Reliability
If the amount of data collected increases, then reliability is improved, but device complexity deteriorates
Solution Approach 1:
The patent segments the complex task of causal relationship detection into distinct processing stages: data collection, model training, and automated detection. By dividing the system into modular components, each handling a specific function, the patent manages the complexity that arises from processing large datasets while maintaining reliable causal relationship determination through specialized processing at each stage.
3Measurement precision
If manual analysis is used, then measurement precision is improved, but loss of time deteriorates
Solution Approach 1:
The patent creates a computational copy of human causal reasoning capabilities through trained models. Instead of requiring actual manual analysis for each dataset, the system uses pre-trained models that replicate human expertise in detecting causal relationships. This copying approach preserves the measurement precision of manual analysis while eliminating the time loss associated with human analysts examining each dataset individually.
Data Source
AI summary
A computer product and methodology is provided for causality reasoning. The computerized method includes determining that a lattice has a first plurality of elements, wherein each element includes at least one pair of cause events and an effect event, and the lattice is configured to relate each one of the first plurality of elements to at least one other element of the first plurality of elements based on the pair of cause events. The method further includes extending the lattice to include a second plurality of events. The method further includes determining a first effect event included in the second plurality of events and corresponding to a first pair of cause events included in the first plurality of elements.