Automatic Document Intake With Adaptive Parsing and Schema Mapping
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Solution Overview
Problem
Processing large documents and volumes of documents across various formats and structures is technologically challenging, time-consuming, and expensive due to the variety in file formats, document structures, and inconsistent handling by recipients.
Innovation Solution
A document transformation and processing system utilizing an interactive graphical user interface with a file section, parser section, and document viewing section, which applies machine learning and artificial intelligence to extract, segment, and map content to predefined or customizable schemas, enabling efficient document intake and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual document processing procedures are used for each recipient, then document processing can be customized to individual needs, but it results in duplicative and inconsistent efforts across the organization
Solution Approach 1:
The system segments document processing into standardized components (parsing pipelines, schemas, extractors) that can be independently configured. Each recipient can select or create specific parsing pipelines tailored to their needs while using the same standardized framework, eliminating redundant development efforts across the organization.
Solution Approach 2:
The platform provides a universal document processing framework that serves multiple recipients and document types through a common set of tools. The standardized schemas and parsing pipelines can be reused across different organizations, reducing overall system complexity and development costs while maintaining customization capabilities.
2Measurement precision
If multiple parsing pipelines are applied to extract content from documents, then content extraction accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system dynamically selects and applies parsing pipelines based on document characteristics and recipient requirements. Rather than applying all possible parsing pipelines universally, the system adapts which pipelines to execute based on the specific document type, content, and organizational needs, optimizing the balance between accuracy and complexity.
Solution Approach 2:
Different parsing pipelines are applied to different sections or types of content within documents based on their specific requirements. The system identifies which parsing methods are most appropriate for specific document sections (e.g., tables vs. text vs. images) and applies only the necessary pipelines to each section, reducing overall processing complexity.
3Reliability
If documents are processed through multiple extraction and mapping stages, then data normalization and usability improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary content extraction and schema mapping in parallel during the document processing pipeline rather than sequentially. Multiple extraction operations are executed simultaneously on different document sections or using different parsing methods, with results being aggregated and normalized in subsequent stages, significantly reducing total processing time.
Solution Approach 2:
The processing pipeline maintains continuous operation by overlapping extraction, mapping, and validation operations. Rather than completing one stage entirely before starting the next, the system continuously processes documents through multiple stages in an overlapping fashion, improving throughput while maintaining data quality through continuous validation.
Data Source
AI summary
A document transformation and processing system and method are provided. An interactive graphical user interface includes a file section and a parser section that displays at least some textual content of an electronic file corresponding to a selection made in the file section. The parser section includes options associated with an electronic file corresponding to the selection made in the file section. An accessed document is assigned to a respective parsing pipeline. At least some content is extracted to a schema and the graphical user interface presents information associated with the selected parsing pipeline, the selected electronic file, and at least some textual content of the selected electronic file. In response to a user selection of at least some of the content of the selected electronic file, the at least one processor highlights mapped output corresponding to the respective schema.