Vectorization Parsing for Complex EDI Files
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Solution Overview
Problem
Existing systems face delays and inefficiencies in processing complexly structured Electronic Data Interchange (EDI) files, particularly due to the computational expense and time required for line-by-line parsing, which is exacerbated by nested data structures in files like EDI 835 and EDI 837.
Innovation Solution
The implementation of a vectorization process to parse EDI files, where data segments are imported into a table as rows and columns, allowing for vectorization processing that populates target tables in a single iteration, significantly reducing computational overhead and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If line-by-line parsing is used to process EDI files, then parsing accuracy is maintained, but processing time and computational expense increase significantly
Solution Approach 1:
The patent segments the EDI file processing into distinct phases: initial rapid scanning to identify segment boundaries, followed by targeted parsing only of relevant segments. This segmentation allows the system to maintain parsing accuracy for critical data while reducing overall processing time by avoiding exhaustive line-by-line analysis of the entire file.
Solution Approach 2:
The patent implements preliminary actions by performing a quick initial scan of the EDI file to identify segment headers, delimiters, and overall structure before conducting detailed parsing. This preliminary structuring enables subsequent processing to focus only on relevant segments, thereby maintaining accuracy while significantly reducing computational expense and processing time.
2Loss of information
If line-by-line parsing is used to process EDI files, then data extraction completeness is ensured, but computational expense increases
Solution Approach 1:
The patent applies partial action by performing complete parsing only on segments that contain critical billing or payment data, while using simplified extraction methods for less important segments. This selective approach ensures data extraction completeness for essential information while reducing computational expense by avoiding exhaustive processing of all file contents.
Solution Approach 2:
The patent implements local quality by applying different parsing strategies to different segments of the EDI file based on their importance and structure. Critical segments receive thorough line-by-line parsing to ensure completeness, while non-critical segments use more efficient extraction methods, thereby balancing data completeness with computational efficiency.
3Adaptability or versatility
If traditional parsing methods are used for complexly structured files, then compatibility with various file formats is maintained, but processing efficiency decreases
Solution Approach 1:
The patent implements universality by creating a multi-functional parsing framework that can automatically detect and adapt to different EDI file formats (such as 835, 837, 270) while maintaining a consistent efficient processing approach. The system includes format detection capabilities and configurable parsing rules that work across multiple file types, thereby maintaining compatibility without sacrificing processing efficiency.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting parsing parameters such as delimiter characters, segment identifiers, and data field positions based on the detected file format. This allows the system to maintain high compatibility with various EDI standards while optimizing processing efficiency for each specific format through parameter customization rather than using a single rigid parsing approach.
Data Source
AI summary
Embodiments of the present disclosure are directed to methods and systems for processing of Electronic Data Interchange (EDI) files. More specifically, processing EDI files includes importing an EDI file into an initial data frame table. Each data segment of the EDI file can be imported into a different row of the initial data frame table and the rows and columns of the initial data frame table can comprise an array of fields. Vectorization processing can then be performed on the array of fields in the initial data frame table. A set of one or more target tables can then be populated with data from the imported EDI file based on performing the vectorization processing on the array of fields in the initial data frame table.