Document Partitioning via Logical Separation Indicators
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Solution Overview
Problem
Unstructured large documents are difficult for end users to navigate due to inefficient manual partitioning, leading to organizational inefficiencies in locating specific information.
Innovation Solution
A method involving a platform that receives and encodes documents, uses a unit extraction service to decode and standardize partition separation characters, assigns metadata keys, indexes these keys and values in a search index, and provides a search function for efficient document partitioning and labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual partitioning is used to categorize information in documents, then the document can be divided into sections, but the partitioning is not fine-grained enough and users still need to read several large sections to locate particular information
Solution Approach 1:
The patent divides documents into fine-grained logical partitions (such as individual paragraphs or sentences) rather than large sections, enabling users to locate specific information more quickly. The system extracts and indexes these small logical units with their corresponding metadata, allowing direct navigation to relevant portions without reading through large sections.
2Productivity
If documents are broken into small logical component parts and labeled, then information can be located efficiently, but the system complexity increases
Solution Approach 1:
The system employs automated machine learning models and natural language processing to perform document partitioning, metadata extraction, and labeling without requiring manual intervention. The automated extraction service analyzes document structure, identifies logical partitions, and assigns metadata automatically, reducing the need for complex manual configuration while maintaining high information location efficiency.
3Extent of automation
If automated extraction services are used to standardize partition separation characters and assign metadata, then document processing becomes more efficient, but the processing complexity increases
Solution Approach 1:
The patent introduces an automated extraction service as an intermediary component that bridges the document input and search index output. This service handles the complex tasks of decoding byte streams, standardizing partition separation characters, extracting metadata, and assigning values to keys, thereby isolating the complexity from the main system while enabling efficient automated document processing.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: determining a first, a second, and a third logical partition separation indicator in a string file, wherein the first logical partition separation indicator is for a first partition level, the second partition separation indicator is for a second level, and the third logical partition separation indicator is also for the first partition level, each in a partition hierarchy; setting a first variable value to a value of the first logical partition separation indicator and a second variable value to a value of the second logical partition separation indicator; writing the first variable value to a data structure and writing the second variable value to the data structure; persisting the data structure to a search index; and clearing the first variable value and the second variable value.