Metadata Extraction from Unstructured Cloud Content
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Solution Overview
Problem
Existing cloud-based content management systems struggle to perform queries on unstructured documents due to their lack of structured format, requiring users to use specialized query languages like SQL, which is cumbersome for users without programming expertise.
Innovation Solution
Implementing a system that uses natural language processing (NLP) to extract metadata from unstructured content in cloud-based content management systems, allowing users to pose queries in everyday language and translating them into a query language format for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional query systems use specialized query languages like SQL, then query processing capability is improved, but user accessibility deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into structured query language. This mediator component allows users to interact with the system using everyday language while the system internally processes sophisticated queries, thus resolving the contradiction between query capability and user accessibility.
Solution Approach 2:
The system changes the parameter of query input from structured syntax requirements to unstructured natural language. By accepting queries in natural language format rather than requiring adherence to query language syntax, the system maintains query processing power while dramatically improving ease of use for non-technical users.
2Adaptability or versatility
If documents are stored in unstructured format, then storage flexibility is improved, but query capability deteriorates
Solution Approach 1:
The system performs preliminary metadata extraction from unstructured documents during the storage process. By extracting relevant information and creating metadata structures in advance, the system enables efficient querying of unstructured content without requiring the documents themselves to be in structured format, thus maintaining storage flexibility while enabling query capability.
Solution Approach 2:
The patent extracts metadata from unstructured document content and separates it into queryable fields. This extraction process pulls out key information from the unstructured text and stores it in a structured metadata format, allowing the main document to remain unstructured for flexibility while the extracted metadata provides query capability.
3Measurement precision
If metadata extraction is performed on all documents, then query accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system applies partial metadata extraction by focusing on extracting only the most relevant metadata fields needed for common queries, rather than extracting all possible information from every document. This selective approach maintains adequate query accuracy for typical use cases while significantly reducing the processing time and computational resources required.
Data Source
AI summary
Disclosed is an improved approach to implement metadata extraction, to extract metadata that can be used for metadata queries. The query may be applied against metadata extracted from content stored in a cloud-based content management system.