Unstructured Document Summarization for Fast Database Retrieval
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Solution Overview
Problem
Knowledge workers face challenges in efficiently understanding and navigating large, constantly changing documents due to their unstructured nature, which consumes significant time and resources.
Innovation Solution
A system utilizing a large language model to analyze large, unstructured documents, generating condensed representations or properties that are stored in a database, enabling efficient access and understanding without consuming the original documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If knowledge workers read and analyze large unstructured documents directly, then they can understand the complete content, but it consumes significant time and resources
Solution Approach 1:
The patent segments large unstructured documents into smaller chunks or sections, processes them individually through the language model to extract key information, and then aggregates these segments into a comprehensive summary. This segmentation allows the system to handle large documents efficiently without requiring workers to read the entire document, thus reducing time loss while maintaining complete content understanding.
Solution Approach 2:
The patent extracts essential information, key concepts, and important details from large unstructured documents using a language model. By taking out only the most relevant information and presenting it in a condensed format, the system enables workers to understand document contents quickly without consuming the entire document, thereby resolving the contradiction between information completeness and time efficiency.
2Loss of information
If knowledge workers manually review and organize multiple documents, then they can identify key takeaways, but it requires significant manual effort and resources
Solution Approach 1:
The patent implements a system where the language model automatically performs the analysis, extraction, and organization of key information from multiple documents without requiring manual intervention. The system serves itself by autonomously processing documents, identifying patterns, and generating synthesized outputs, thereby eliminating the need for knowledge workers to manually review and organize documents while still achieving comprehensive key takeaway identification.
Solution Approach 2:
The patent replaces the mechanical manual process of reading, analyzing, and organizing documents with an automated language model system. This substitution transforms the manual cognitive effort into an automated computational process, maintaining the quality of key information identification while dramatically reducing the effort and resources required from knowledge workers.
3Loss of information
If the system stores and processes complete large documents, then all information is available, but storage and processing costs increase
Solution Approach 1:
The patent extracts only the essential information, key concepts, and critical details from large documents and stores these extracted elements rather than the complete original documents. This extraction approach maintains full information availability for analysis and retrieval while significantly reducing the storage space required, as only the most valuable information components are preserved in the database.
Solution Approach 2:
The patent inverts the traditional approach by not storing complete documents and then searching through them, but instead storing pre-processed extracted information that can be directly queried and synthesized. This inversion allows the system to maintain comprehensive information availability while using minimal storage resources, as the stored extracted data is specifically optimized for retrieval and analysis rather than storing redundant complete document copies.
Data Source
AI summary
The system obtains a record in a database and a property associated with the record in the database, where the record includes a large document, and where the large document is unstructured or semi-structured. The system receives an input indicating a type of analysis to perform associated with the record and performs, using an artificial intelligence, the analysis associated with the record to obtain an output. The type of analysis to obtain the output includes generating a document describing contents of the record, where the document describing the contents of the record is smaller than the record. The system stores the output as the property in the database and enables access to the database based on the property, thereby enabling an efficient understanding of contents of the document without consuming the document.