Machine Learning Data Annotation System for Unstructured Documents
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Solution Overview
Problem
Current data annotation processes for machine learning systems are inefficient, particularly for small organizations with limited resources, as they require manual data entry and lack automated tools for transforming unstructured data into structured formats, which hampers their ability to effectively market properties and engage with the commercial real estate industry.
Innovation Solution
The Machine Learning Data Annotation (MLDA) system automates data annotation by using Natural Language Processing (NLP) and Machine Learning (ML) to extract structured data from unstructured sources like PDFs, emails, and websites, enabling the creation of annotated data representations and interactive flyers without manual entry, and integrates with marketing engines to promote properties online.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used for data annotation, then data can be annotated with human oversight, but the process is inefficient and time-consuming
Solution Approach 1:
The patent replaces manual mechanical data entry operations with automated machine learning systems that use natural language processing and classification algorithms to extract and annotate data from unstructured sources, dramatically improving efficiency while reducing time loss
Solution Approach 2:
The system enables self-service data annotation where the machine learning model automatically processes and annotates data without requiring continuous human intervention, allowing the system to serve itself in performing annotation tasks
2Productivity
If automated tools are used to transform unstructured data into structured formats, then data processing efficiency improves, but implementation complexity increases for small organizations
Solution Approach 1:
The patent creates a universal machine learning platform that can handle multiple data types and annotation tasks through a single system, reducing implementation complexity for small organizations by providing multi-functional capabilities rather than requiring separate specialized tools
Solution Approach 2:
The system introduces an intermediary machine learning layer that sits between unstructured data sources and structured data requirements, automatically performing transformation and annotation while simplifying the interface for end users
3Extent of automation
If machine learning systems are used for data annotation, then automation and accuracy improve, but the initial setup and training requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with existing data and pre-configuring annotation schemas before deployment, reducing setup complexity for new projects while maintaining high automation capabilities
Solution Approach 2:
The system allows flexible parameter adjustment in the machine learning models to adapt to different data types and requirements, simplifying setup by enabling parameter tuning rather than requiring complete system reconfiguration
Data Source
AI summary
The MACHINE LEARNING DATA ANNOTATION APPARATUSES, METHODS AND SYSTEMS (“MLDA”)discloses a processor-implemented confidence structured output document creation method which comprises, in one embodiment, receiving a unknown inconsistent structured document and receiving an confidence information extraction feature. The MLDA may parse the unknown inconsistent structured document to retrieve data field tags and data field values and process the data field tags and the data field values with the confidence information extraction feature. The MLDA may extract processed data field tags and data field values, and provide processed data field tags and data field values to a confidence structured output document learning engine. The MLDA may retrieve a confidence structured output document web form template, populate the confidence structured output document web form template with the extracted data field tags and data field values to generate a confidence structured output document, and provide the confidence structured output document.