Multi-modal EDI Encoder for Text-EDI Embedding
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Solution Overview
Problem
The exchange of electronic data interchange (EDI) files across different systems is hindered by format discrepancies, making it difficult for machine-based processing and natural language understanding, as the underlying formatting obscures substantive content and impedes data retrieval and cluster analysis.
Innovation Solution
A multi-modal machine learning model that embeds both text and EDI data in a common embedding space using a transformer-based encoder network with expert networks, allowing for the generation of common embeddings for various EDI formats and enabling context-aware querying and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional EDI formatting is used for data exchange, then data can be transmitted between systems, but the formatting obscures substantive content and makes machine-based processing and natural language understanding difficult
Solution Approach 1:
The patent introduces an embedding space as an intermediary layer between EDI data and natural language text. This embedding space transforms both EDI segments and text into a common vector representation, allowing machine learning models to process and understand the substantive content without being hindered by the original EDI formatting structure.
Solution Approach 2:
The patent applies parameter changes by transforming EDI data from its original formatted structure into embedding vectors with different dimensional parameters. The multi-modal encoder converts EDI segments into a continuous vector space where semantic meaning is preserved but the original formatting constraints are removed, enabling new types of processing and analysis.
2Adaptability or versatility
If multiple EDI formats are supported, then versatility in data exchange is improved, but difficulty in detecting and measuring content similarity increases
Solution Approach 1:
The patent creates a universal embedding space that can represent multiple EDI formats (X12, EDIFACT, cXML) and natural language text simultaneously. The multi-modal encoder is designed to handle different input types through a unified architecture, allowing content similarity detection across format boundaries using the same vector comparison mechanisms.
Solution Approach 2:
The patent resolves format diversity by projecting all EDI formats and text into a higher-dimensional embedding space. This dimensional transformation allows semantically equivalent content from different formats to converge to similar vector representations, making similarity detection straightforward through vector distance or cosine similarity calculations.
3Measurement precision
If format-specific processing is used for different EDI types, then processing accuracy is improved, but system complexity and routing requirements increase
Solution Approach 1:
The patent segments the processing architecture into specialized expert networks, where each expert is trained on specific EDI formats or content types. The router layer divides incoming EDI segments and directs them to appropriate experts based on format identification, allowing each expert to maintain high processing accuracy for its specialized domain while the overall system handles diverse formats.
Solution Approach 2:
The patent implements dynamic routing where the router layer adaptively directs EDI segments to appropriate expert networks based on the detected format or content characteristics. This dynamic allocation allows the system to optimize processing accuracy for each segment type while maintaining flexibility to handle new or uncommon formats by routing them to general-purpose experts.
Data Source
AI summary
Multi-modal representation learning for electronic data interchange (EDI) and text includes encoding a text sample with a text encoder. The encoding creates an embedded text sample in a conjoined embedding space. An EDI sample is encoded with a multi-model EDI encoder. Encoding the EDI sample creates an embedded EDI sample in the conjoined embedding space. The multi-model EDI encoder includes a plurality of expert networks. The multi-modal EDI encoder is trained using machine learning to determine a similarity score between a target text and an EDI segment. The machine learning includes comparing labeled training samples and corresponding predictions generated by the multi-model EDI encoder. Once trained, the multi-model EDI encoder is configured to query a data repository of EDI documents for one or more selected EDI documents that match a natural language text input.