Feature-Specific Attention Arrays for Efficient Event Sequence Embeddings
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Solution Overview
Problem
Transformer models face computational inefficiencies due to quadratic scaling of self-attention mechanisms with sequence length and increased computing costs when handling feature data, which can diminish the ability to explain downstream results.
Innovation Solution
Implementing attention arrays that capture relationships between features of different events by generating intelligent attention masks to associate events and their features, reducing computational complexity and enhancing feature data utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all events in the sequence are captured by flattening the sequence, then complete event data is captured, but computational complexity increases quadratically with sequence length
Solution Approach 1:
The patent segments the event sequence processing by introducing feature-specific attention arrays that operate on individual feature dimensions separately. Instead of computing attention over the entire flattened sequence, the system divides attention computation into feature-specific segments, reducing the quadratic complexity while preserving complete event data through structured feature organization.
Solution Approach 2:
The patent transforms the problem from a one-dimensional sequence processing task into a multi-dimensional feature space by organizing events according to their feature types. This dimensional transformation allows the system to capture complete event information while computing attention efficiently across feature dimensions rather than sequence positions, mitigating the quadratic scaling issue.
2Loss of information
If events are encoded into an embedding space, then feature data is captured, but additional computing costs are incurred for training and data management
Solution Approach 1:
The patent employs self-attention mechanisms that allow the model to automatically learn and weigh feature relationships without requiring external encoding into embedding spaces. The feature-specific attention arrays enable the system to self-determine which features are important for each event, eliminating the need for separate training processes to create feature embeddings and reducing overall computing costs.
3Loss of information
If events are encoded into an embedding space, then feature data is captured, but the ability to explain downstream results decreases
Solution Approach 1:
The patent enhances explainability by organizing attention computations into feature-specific categories that can be interpreted and visualized. Each feature-specific attention array can be analyzed to understand which features contributed most to predictions, providing clear insights into model decision-making. This structured feature organization maintains explainability while capturing complete feature data, unlike opaque embedding transformations.
Data Source
AI summary
A method and related system for efficiently capturing relationships between event feature values in embeddings includes flattening an event sequence into a feature sequence including a first event prefix, a second event prefix, and a first set of feature values. The method includes generating an attention mask including first mask indicators to associate the first set of feature values with each other and second mask indicator to associate a first feature value of the first set of feature values with the second event prefix. The method includes providing the feature sequence and the attention mask to a self-attention neural network model to generate an embedding.