Entity Detection Service for Unstructured Text Analysis
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Solution Overview
Problem
Current business intelligence systems are unable to effectively utilize unstructured alphanumeric data due to its disorganized format, varying detail, and lack of explicit structure, leading to inefficiencies in data analysis and error-prone manual tagging processes.
Innovation Solution
A service that employs machine learning models for synchronous and asynchronous entity and relationship detection from unstructured text, segmenting data, and providing token information to identify entities and relationships, enabling structured data extraction and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tagging is used to structure unstructured data, then data can be organized for business intelligence applications, but the process is completely impractical for large amounts of data and produces significant errors
Solution Approach 1:
The patent replaces manual mechanical tagging processes with automated machine learning-based entity detection systems. The system uses trained models to automatically identify and classify entities in unstructured text, substituting human labor with computational processes that scale efficiently while maintaining or improving accuracy through consistent application of detection rules.
Solution Approach 2:
The system enables unstructured data to be automatically processed and structured through self-service mechanisms. The entity detection service autonomously analyzes text, identifies entities, and returns structured results without requiring manual intervention, allowing the data processing pipeline to serve itself.
2Productivity
If automated tagging software is deployed, then processing speed increases, but the systems introduce many errors and only work for specific use cases
Solution Approach 1:
The entity detection service is designed with universal applicability across multiple domains and use cases. The system can detect various entity types (organizations, locations, persons, medical entities, etc.) in different text formats and contexts, making it adaptable to diverse business intelligence applications rather than being limited to specific scenarios.
Solution Approach 2:
The system incorporates confidence scores in its entity detection results, providing feedback on the reliability of each detection. This allows downstream processes to filter or review low-confidence detections, improving overall accuracy while maintaining high processing speed for high-confidence results.
3Quantity of substance
If unstructured data is used directly in business intelligence applications, then data volume is preserved, but the applications cannot extract base data for analytics due to lack of schema or data descriptors
Solution Approach 1:
The system extracts structured entity information from unstructured text data. By identifying and extracting entities with their types, attributes, and relationships, the service pulls out the essential structured data needed for business intelligence analytics while preserving the original unstructured data volume for reference.
Solution Approach 2:
The entity detection service acts as an intermediary layer between unstructured data sources and business intelligence applications. It transforms unstructured text into structured entity data that BI applications can consume, bridging the gap between data volume preservation and data usability.
4Device complexity
If current business intelligence systems are applied to unstructured data, then existing infrastructure is utilized, but the systems fail to extract any base data on which analytics can be run
Solution Approach 1:
The entity detection service performs preliminary structuring of unstructured data before it reaches business intelligence applications. By pre-processing the data to extract entities and relationships, the system prepares the data in advance, enabling downstream BI systems to function reliably without requiring complex modifications to their existing infrastructure.
Data Source
AI summary
Techniques for entity and relationship detect from unstructured text as a service are described. A service may receive a request to identify entities within a provided unstructured text element, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities. The service may send additional requests to an additional service or services implementing additional deep machine learning models to identify relationships between detected attributes and ones of the detected entities. The outputs from all services can be analyzed and consolidated into a single result that identifies the entities, any attributes of the entities, and confidence scores indicating the confidence in each detected entity.