AI Document Chunking for Semantic Role Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current document authoring systems lack the ability to efficiently identify and utilize semantically significant chunks within documents, such as names, dates, and addresses, which are crucial for business processes, due to the absence of explicit representation of datatypes and semantic roles, leading to manual labor and errors in document creation and data transfer.
Innovation Solution
The use of machine learning and artificial intelligence to automatically identify and assign datatypes and semantic roles to chunks within documents, leveraging context and patterns across similar documents to enhance document creation and processing, including the development of hierarchically semantically labeled documents that can be used in downstream business processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual editing and replacement of chunks is used in document creation, then document customization is achieved, but time consumption and error rate increase
Solution Approach 1:
The patent replaces manual mechanical editing with an automated AI-based system that uses natural language processing and machine learning to identify and replace chunks. The system automatically detects semantically significant chunks (names, dates, addresses) and performs replacements based on learned patterns from similar documents, eliminating manual labor while maintaining high accuracy through contextual understanding.
Solution Approach 2:
The system enables documents to self-identify their semantic chunks and self-perform replacements based on patterns learned from the document corpus. The AI model automatically analyzes document structure, identifies chunk boundaries, and executes replacements without human intervention, allowing the document processing system to serve itself.
2Adaptability or versatility
If forms and templates are used for document creation, then structure standardization is achieved, but adaptability to changing circumstances decreases
Solution Approach 1:
The patent transforms static forms and templates into dynamic, adaptive structures. The system learns from actual document variations and automatically adjusts chunk identification and replacement strategies based on contextual patterns. This allows the system to adapt to changing document requirements without manual reconfiguration of forms or templates.
Solution Approach 2:
The system changes the parameters of document processing by moving from fixed template-based approaches to flexible, context-aware chunk-based processing. The AI model dynamically adjusts identification and replacement parameters based on learned patterns from the document corpus, enabling adaptability without increasing system complexity.
3Loss of information
If word processing formatting markers are used, then formatting control is achieved, but semantic role identification is lost
Solution Approach 1:
The patent extracts semantic role information from the document content itself rather than relying on external formatting markers. The AI system directly identifies semantic roles (buyer, seller, address, date) by analyzing contextual patterns, relationships, and language structures within the document text, separating semantic identification from formatting control.
Solution Approach 2:
The system makes the document text itself multi-functional by enabling it to simultaneously convey both formatting information and semantic role information. The AI model learns to interpret the text in multiple ways - for formatting purposes and for semantic understanding - eliminating the need for separate markup systems.
Data Source
AI summary
Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.