Semantic Claim Document Analysis for Workflow Recommendations
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Solution Overview
Problem
Existing natural language processing (NLP) techniques struggle with accurately extracting domain-specific insights from unstructured insurance claim documents due to grammatical imperfections, repetitive boilerplate text, and context-dependent word meanings, leading to false positives and negatives, especially in identifying critical events like threats to seek legal action, which can escalate costs and workload.
Innovation Solution
A computerized method involving domain-specific sentence analysis, semantic ontology using a graph database, and machine learning to tag sentences, apply predefined ontologies, and combine hashtags for insights extraction, addressing grammatical noise and context nuances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword and phrase search techniques are used to extract topics and events, then the extraction process is simple and fast, but false positives increase due to template and boiler plate text
Solution Approach 1:
The patent introduces sentence embedding models and semantic similarity computation as intermediary layers between the search query and the text corpus. These intermediaries transform keywords into semantic representations and compute meaningful similarity scores, filtering out false positives from template text while maintaining extraction speed through efficient vector operations.
Solution Approach 2:
The patent replaces traditional mechanical keyword matching with neural network-based semantic analysis. Sentence embedding models transform text into continuous vector representations, enabling the system to understand semantic meaning rather than relying on exact keyword matches, thereby reducing false positives while maintaining scalability.
2Reliability
If semantic interpretation and context analysis are applied to distinguish nuanced text variations, then extraction accuracy improves, but processing complexity and time increase
Solution Approach 1:
The patent segments the processing task into distinct stages: sentence tokenization, embedding generation, similarity computation, and threshold-based filtering. This segmentation allows each component to be optimized independently and enables parallel processing, reducing overall complexity while maintaining high extraction accuracy through systematic analysis.
Solution Approach 2:
The patent changes the parameter space from discrete keyword matching to continuous semantic vector space. By transforming text into embedding vectors and computing cosine similarity, the system captures nuanced contextual differences with a single computational operation, simplifying the complexity of semantic interpretation while improving accuracy.
3Productivity
If traditional topic extraction techniques like LDA are used, then processing is efficient, but rare but significant events are missed because they appear in only a minority of documents
Solution Approach 1:
The patent introduces semantic embedding as an intermediary that transforms the topic extraction process. Instead of relying on co-occurrence statistics that favor frequent topics, the embedding model captures semantic relationships that allow rare but significant events to be identified through their unique semantic signatures, preventing information loss while maintaining efficiency.
Solution Approach 2:
The patent moves the analysis from the traditional topic-space dimension to a semantic embedding dimension. This dimensional transformation allows the system to detect rare events by their semantic characteristics rather than frequency, enabling the identification of critical but infrequent topics that would be invisible to traditional LDA approaches.
Data Source
AI summary
A computerized method for extracting domain specific insights from a corpus of files containing large documents comprising: breaking down large chunks of text into smaller sentences/short paragraphs in a domain specific way, identifying and removing domain noise; identifying the sentence intents of the non-noise sentences; tagging the sentences with other domain specific attributes; defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes; applying a pre-defined ontology to tag documents with domain specific hashtags; and combining the hashtags using machine learning techniques into insights.