Self-Attention Matching Between Disparate Datasets at Quadratic Scale
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Solution Overview
Problem
Computational matching between disparate datasets, particularly in one-to-many scenarios, is computationally prohibitive due to indirect references and exponential calculation requirements, exceeding the capacity of modern computers.
Innovation Solution
The method involves vectorizing datasets into embedded representations and applying self-attention models to generate attention datasets, which are combined into a relationship matrix using dot product operations to efficiently identify matching relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational matching methods are used for one-to-many matching between disparate datasets, then matching accuracy can be maintained, but computational complexity becomes exponentially prohibitive
Solution Approach 1:
The patent introduces an intermediary representation layer (embeddings) that transforms disparate datasets into a common computational space. This intermediary layer enables efficient matching by converting complex direct comparisons into simpler operations within the embedded representation space, reducing computational complexity while maintaining matching accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of data representation by transforming raw data into embedded representations with specific dimensional characteristics. This parameter transformation allows the system to operate in a reduced computational space where matching operations become polynomial-time feasible rather than exponential-time prohibitive.
2Ease of manufacture
If direct comparison methods are used to match datasets, then implementation simplicity is maintained, but computational capacity requirements exceed modern computer capabilities
Solution Approach 1:
The patent replaces the mechanical brute-force comparison system with a neural network-based embedding system. This substitution transforms the computational approach from direct mechanical comparison of all data pairs to a learned representation system that efficiently captures relationships, reducing computational capacity requirements while maintaining implementation feasibility.
Solution Approach 2:
The patent performs preliminary action by pre-computing embedded representations of datasets before the actual matching task. This preliminary processing transforms the data into a ready-to-match format that requires significantly less computational capacity during the matching operation itself, making the system feasible for modern computers.
3Measurement precision
If constraint additions are made to the matching problem (e.g., dollar amount matching), then matching precision improves, but computational complexity increases exponentially
Solution Approach 1:
The patent merges multiple matching constraints and criteria into a unified embedding representation space. By combining the effects of multiple constraints (such as dollar amount matching, date matching, and entity matching) into a single representation framework, the system achieves high matching precision without the exponential complexity increase that would result from handling each constraint separately.
Data Source
AI summary
A method including receiving a command to perform a one-to-many matching task between first and second datasets. The first and second datasets are vectorized into first and second embedded datasets. A first self-attention model is executed on the first embedded dataset to generate a first attention dataset in which each value of a first number of first features of the first dataset is weighted based on each other value of the first number of first features. A second self-attention model is executed on the second embedded dataset to generate a second attention dataset in which each value of a second number of second features of the second dataset is weighted based on each other value of the second number of second features. The first and second attention datasets are combined into a relationship matrix expressing relationships between the first and second features. The method also includes returning the relationship matrix.