Automated Document Indexing for Large File Assessment
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Solution Overview
Problem
Large files, such as health records or insurance claims, often consist of thousands of pages, leading to delays and missed information during assessment processes, as they are typically compiled manually into reports.
Innovation Solution
A document index generating system and method that preprocesses pages into data structures, classifies them into document types, segments groups into documents, and generates page and document indices, as well as a document summary generating system that clusters content, determines central points, and generates summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compilation of reports from large files is performed, then human review and assessment can be conducted, but delays and missed information occur due to the large number of pages
Solution Approach 1:
The system segments large files into individual pages and groups of pages, then automatically classifies and indexes them. This division allows the system to process manageable portions of the document systematically, enabling comprehensive review without manual handling of the entire large file, thus reducing time loss while maintaining assessment accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of reading and compiling reports with an automated computer-based system that uses optical character recognition, classification algorithms, and natural language processing. This substitution eliminates human reading time constraints and reduces missed information while maintaining thorough assessment capability.
2Reliability
If manual report compilation is performed, then contextual understanding and professional judgment can be applied, but productivity is reduced due to the time-consuming nature of the task
Solution Approach 1:
The system replaces manual report compilation with automated text recognition and classification processes that can process multiple pages simultaneously. This mechanical substitution maintains quality through algorithmic consistency while dramatically increasing productivity by processing files at speeds impossible for human reviewers.
Solution Approach 2:
The system performs self-service by automatically extracting, classifying, and organizing information from large files without requiring manual intervention for each page. The automated classification and indexing processes enable the system to serve its own information organization needs, freeing human reviewers to focus on high-level assessment decisions rather than manual compilation tasks.
3Productivity
If automated processing is implemented, then productivity and speed are improved, but complexity of the system increases
Solution Approach 1:
The complex automated processing system is divided into distinct functional modules: optical character recognition, text classification, grouping, and indexing. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while enabling high-speed automated processing through coordinated operation of specialized subsystems.
Solution Approach 2:
The system employs universal components that perform multiple functions - for example, the classification module both categorizes pages and extracts key information, while the indexing system simultaneously organizes pages and creates searchable databases. This multi-functionality reduces the number of separate systems needed, thereby reducing overall complexity while maintaining high productivity.
Data Source
AI summary
A document index generating system and method are provided. The system comprises at least one processor and a memory storing a sequence of instructions which when executed by the at least one processor configure the at least one processor to perform the method. The method comprises preprocessing a plurality of pages into a collection of data structures, classifying each preprocessed page into at least one document type, segmenting groups of classified pages into documents, and generating a page and document index for the plurality of pages based on the classified pages and documents. Each data structure comprises a representation of data for a page of the plurality of pages. The representation comprises at least one region on the page.