NLP Engine for Parsing Unstructured Experience Data
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Solution Overview
Problem
Current systems fail to efficiently process and analyze structured and unstructured experience data from diverse sources like medical records, surveys, and social media, leading to inaccurate and incomplete insights for healthcare and employer organizations due to the complexity of language and varying data formats, resulting in unutilized valuable information.
Innovation Solution
A system utilizing a hybrid Natural Language Processing (NLP) pipeline combined with machine learning and crowd sourcing to identify sentiments, themes, and named entities within the data, transforming unstructured data into structured and ordered information for visualization on user dashboards, providing actionable business intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing systems are used to handle structured and unstructured experience data, then the system architecture is simple, but the data processing accuracy and completeness deteriorate due to inability to handle language complexity and varying data formats
Solution Approach 1:
The patent segments the data processing system into multiple specialized components: an NLP engine for unstructured text data, a data mining engine for pattern recognition, and a relational database for structured storage. Each component handles specific data types and processing tasks, enabling accurate processing of complex language data while maintaining modular system architecture that manages complexity through division of labor.
Solution Approach 2:
The patent introduces an NLP engine as an intermediary component between the raw unstructured data and the analysis systems. This intermediary transforms unstructured text into structured concepts and relationships, bridging the gap between diverse data formats and the analytical processing requirements, thereby improving data accuracy without requiring complete system redesign.
2Productivity
If manual data analysis methods are used, then the system complexity is low, but the processing speed and efficiency deteriorate significantly
Solution Approach 1:
The patent implements automated self-service processing through the NLP engine and data mining engine, which automatically parse, annotate, and analyze experience data without manual intervention. The system autonomously handles text preprocessing, concept extraction, relationship identification, and pattern recognition, dramatically improving processing speed while the modular architecture keeps system complexity manageable through specialized automated components.
3Loss of information
If comprehensive data collection from multiple sources is implemented, then the information completeness improves, but the data integration complexity and processing difficulty worsen
Solution Approach 1:
The patent creates a universal processing framework that handles multiple data types (structured and unstructured) from diverse sources (medical records, surveys, social media) through a common architecture. The NLP engine and data mining engine serve as multi-functional components that process various data formats uniformly, enabling comprehensive data collection while reducing integration complexity through standardized processing pathways.
Solution Approach 2:
The patent combines multiple processing engines with different capabilities into a composite system: the NLP engine handles unstructured text, the data mining engine identifies patterns, and the relational database stores structured results. This composite architecture integrates diverse data sources effectively by assigning specialized processing functions to each component, achieving information completeness while managing integration complexity through functional composition.
4Measurement precision
If advanced NLP and data mining engines are deployed, then the insight accuracy and actionable intelligence improve, but the computational resource requirements and processing complexity worsen
Solution Approach 1:
The patent segments the computational workload across specialized engines that process different aspects of data analysis independently. The NLP engine focuses on text parsing and annotation, while the data mining engine concentrates on pattern recognition and relationship identification. This segmentation enables accurate insights through specialized processing while optimizing resource consumption by avoiding redundant computations across the entire system.
Data Source
AI summary
A system and method for processing and actionizing structured and unstructured experience data is disclosed herein. In some embodiments, a system may include a natural language processing (NLP) engine configured to transform a data set into a plurality of concepts within a plurality of distinct contexts, and a data mining engine configured to process the relationships of the concepts and to identify associations and correlations in the data set. In some embodiments, the method may include the steps of receiving a data set, scanning the data set with an NLP engine to identify a plurality of concepts within a plurality of distinct contexts, and identifying patterns in the relationships between the plurality of concepts.