Document Analyzer Automating Multi-Format Layout Parsing
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Solution Overview
Problem
Current solutions for analyzing word-processing documents are inefficient and labor-intensive, as they require manual review and are not capable of handling documents with different formats and contents, lacking the ability to perform automated analysis across multiple documents.
Innovation Solution
A system where computers are programmed to analyze multiple word-processing documents based on user-input rules, identifying specific layout structures and performing actions on them, such as removing images and converting documents to markup language, to extract and analyze content across multiple documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of documents is performed, then analysis accuracy can be maintained, but productivity and efficiency deteriorate due to labor-intensive processes
Solution Approach 1:
The system enables automated self-analysis of documents through programmed computers that automatically identify layout structures, extract content, and perform analysis without human intervention. The document analyzer independently processes multiple documents by detecting structures based on identifiers and applying predetermined actions, eliminating the need for manual review while maintaining accuracy through rule-based automation.
2Ease of manufacture
If prior art solutions are used for document extraction, then specific format documents can be processed, but adaptability deteriorates as code rewriting is needed for different formats
Solution Approach 1:
The system creates a universal document analyzer that can process multiple document formats and structures through a single program. The analyzer identifies layout structures by searching for identifiers and applies predetermined actions based on structure type, enabling it to handle tables, sections, and other formats without requiring code rewriting. This multi-functional approach allows the same system to adapt to different document types.
Solution Approach 2:
The system changes the approach from format-specific hardcoding to parameter-based identification. Instead of writing separate code for each document format, the analyzer uses configurable parameters such as identifier names and structure types to adapt to different formats. This allows the system to process various document structures by changing identification parameters rather than rewriting core code.
3Productivity
If automated extraction is implemented, then productivity improves, but measurement precision deteriorates due to inability to perform actionable analysis
Solution Approach 1:
The system performs continuous automated analysis by chaining multiple predetermined actions together. After extracting content from identified layout structures, the system automatically performs additional actions such as counting rows, analyzing text patterns, and generating insights. This continuous automated processing maintains both high productivity and deep analysis capability by eliminating idle time between extraction and analysis steps.
Data Source
AI summary
One or more computers receive input indicative of multiple files to be analyzed together, by performing one or more predetermined actions, using the contents (e.g. strings of text) of a corresponding one or more structures. The one or more structures are identified by the presence in each file, of corresponding names. The one or more structures are normally written into the files for use by an application program to layout the contents therein in a structured manner. The one or more computers are programmed to automatically parse each file, to identify therein the one or more layout structures e.g. based on the presence in each file of corresponding names of layout structures. After parsing, the one or more computer(s) perform the one or more predetermined actions, to obtain an output structure that holds the results based on the contents of each layout structure identified in each file.