Extraction Prompt Enrichment for Complex Document Field Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document data extraction methods face challenges such as incorrect value extraction, hallucination of values, and failure to extract fields due to incorrect interpretation of missing values or incorrect association of fields and values, particularly in complex document layouts.
Innovation Solution
The implementation enriches extraction prompts using contextual data and machine learning models like LLMs to generate structured documents by defining data field ranges and using prediction engines for precise data extraction, ensuring accurate and efficient data extraction by considering allowed values and ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional extraction methods are used on complex document layouts, then processing speed is maintained, but data extraction accuracy deteriorates due to incorrect value extraction and hallucination
Solution Approach 1:
The patent introduces an intermediary enrichment system that processes extraction prompts before they reach the machine learning model. This intermediary layer adds contextual data, schema validation, and range constraints to the prompts, thereby improving extraction accuracy without requiring fundamental changes to the underlying ML model architecture.
Solution Approach 2:
The system performs preliminary actions by enriching extraction prompts with contextual data, allowed values, and range constraints before the actual data extraction occurs. This pre-processing of extraction parameters ensures that the ML model receives well-structured, contextually-enriched prompts that guide more accurate extraction while maintaining system simplicity.
2Measurement precision
If enrichment processing is added to extraction prompts, then data extraction accuracy improves through contextual data and range definitions, but processing time increases
Solution Approach 1:
Contextual data and range constraints are pre-computed and stored in the enrichment database before extraction operations occur. When extraction is needed, the system quickly retrieves pre-prepared enrichment data rather than computing it in real-time, thereby improving accuracy while minimizing additional processing time.
3Reliability
If contextual data and range constraints are added to prompts, then extraction reliability improves by reducing hallucination, but storage requirements increase
Solution Approach 1:
The enrichment database stores contextual data and range constraints in a localized, organized manner specific to each data field and document type. Rather than storing all possible data universally, the system maintains only the relevant contextual information needed for each specific extraction scenario, improving reliability while optimizing storage efficiency.
Data Source
AI summary
The disclosure generally describes methods, software, and systems for document data extraction. A digitalized document in a layout-preserving text representation is obtained. A document type of the digitalized document can be determined based on its structural characteristics. Contextual data related to the document type of the digitalized document can be obtained. A prompt comprising a data field extraction schema corresponding to the document type of the digitalized document can be generated. The prompt can be enriched based on the contextual data and the respective layout of the data fields in the digitalized document. The enriched prompt can define ranges for one or more data fields. A structured document can be obtained from the execution of a prediction engine based on the enriched prompt invoked for the digitalized document. The structured document can be provided for semantic querying to extract portions of data from the structured document.