Machine Learning Data Extraction for Document Information Retrieval
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Solution Overview
Problem
Users of documents, such as news articles, face limitations in accessing comprehensive data related to the subject matter, as relevant information is often scattered throughout the text and not easily retrievable.
Innovation Solution
A client-server-based network architecture that utilizes machine learning models to parse and extract relevant data from source documents, providing it to users in an automated and unified manner through a graphical user interface, integrating with data repositories to offer graphical representations and numerical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If readers access information only from the source document text, then information completeness is maintained within the document boundaries, but information accessibility and comprehensiveness deteriorate because relevant data is scattered and hard to retrieve
Solution Approach 1:
The patent introduces an intermediary system comprising a processing module and data repository that mediates between the source document and the reader. The processing module extracts entities and concepts from the document, queries the data repository for related information, and presents consolidated results to the reader, thereby improving accessibility without losing information completeness
Solution Approach 2:
The system segments the information retrieval process into distinct components: document parsing, entity extraction, data repository querying, and result presentation. This segmentation allows each component to specialize in specific tasks, improving overall information accessibility while maintaining completeness
2Reliability
If factual information is distributed throughout the article text, then information context is preserved, but information retrieval efficiency deteriorates because readers must manually search through the entire document
Solution Approach 1:
The processing module extracts key entities, concepts, and factual information from the distributed text into a structured format. By taking out this information from its scattered locations in the source document and organizing it systematically, the system enables efficient retrieval while preserving the contextual relationships through the data repository structure
Solution Approach 2:
The system transforms the one-dimensional linear text structure into a multi-dimensional information space by creating associations between entities, concepts, and related data in the repository. This dimensional transformation allows readers to access information from multiple entry points and perspectives, improving retrieval efficiency while maintaining contextual integrity
3Loss of information
If the system integrates external data repositories to provide comprehensive related data, then information comprehensiveness is improved, but system complexity deteriorates due to integration requirements
Solution Approach 1:
The data repository is designed as a universal multi-functional component that can store and provide various types of related information (statistical data, contextual information, supplementary facts) for different domains and topics. This universality reduces system complexity by using a single integrated repository rather than multiple specialized databases
4Measurement precision
If the system automatically extracts and presents related data from source documents, then information retrieval accuracy is improved, but processing time increases due to automated analysis requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing the source document content before user queries are submitted. Entities, concepts, and key information are extracted and stored in an optimized format in advance, so that when users seek information, the system can quickly retrieve and present accurate results without performing extensive real-time analysis
Data Source
AI summary
Methods and systems of providing related information to a source document are described. The method may include accessing the source document displayed to a user in a graphical user interface (GUI) of a client device. The source document includes numerical data and text. Discovered data corresponding to the numerical data included in the source document is then identified. Further, a database trained with a machine-learning algorithm to identify time series data related data associated with the text is accessed. The discovered data with a discovered data identifier and the time series related data is then displayed in the GUI. In example embodiments, the methods and systems described herein interact with applications such as spreadsheets applications, email clients, word processing applications, webpages and the like.