Search Term Extraction from Natural Language Text
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Solution Overview
Problem
Large and complex marketing databases require faster and more accurate translation of natural-language target audience descriptions into optimal search terms, as human-curated searches are impractical due to high error rates and variable turnaround times in the rapidly evolving marketing environment.
Innovation Solution
A system that extracts and structures meaningful search terms from natural language descriptions by identifying keywords, optimizing search results, and capturing relevant data elements using machine learning and in-memory processing to generate optimized search queries for marketing databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human curators manually translate natural-language RFP descriptions into search terms, then the search process can be completed with human interpretive judgment, but the turnaround time becomes too slow and error rates increase in the rapidly evolving marketing environment
Solution Approach 1:
The patent replaces the mechanical human cognitive process of translating natural language to search terms with an automated natural language processing system. The system uses computational algorithms to parse RFP documents, identify key terms, and generate optimized search queries, eliminating the time-consuming manual process while maintaining or improving accuracy through consistent application of NLP rules and machine learning models.
Solution Approach 2:
The system enables the RFP document itself to serve as the source for generating search terms through automated text analysis. The natural language processing system extracts and structures search terms directly from the RFP content without requiring external human intervention, allowing the document to effectively translate itself into database search queries.
2Reliability
If human curators manually search through thousands of data segments, then they can apply interpretive heuristics to identify relevant data, but the process cannot keep pace with the rapidly increasing number of searches required
Solution Approach 1:
The patent replaces manual human searching through thousands of data segments with an automated system that uses natural language processing to generate and execute search queries. The system can process multiple RFPs simultaneously, generating optimized search terms for each and retrieving relevant data segments at machine speed, thereby dramatically increasing throughput while maintaining reliability through systematic search coverage.
Solution Approach 2:
The system performs preliminary processing of RFP documents by automatically extracting and structuring search terms before the actual data search begins. This preprocessing step prepares optimized queries in advance, allowing the search system to execute efficiently without manual intervention during the search execution phase, thereby increasing overall productivity.
3Adaptability or versatility
If human curators are employed to translate RFPs into search queries, then the process can handle complex natural-language descriptions, but the cost of the process increases significantly
Solution Approach 1:
The patent replaces expensive human curator labor with an automated natural language processing system that can handle complex RFP descriptions at minimal marginal cost. The system uses computational resources to parse, understand, and translate natural language requirements into search queries, eliminating the need to pay human curators for each RFP processing while maintaining the ability to handle complex, nuanced descriptions through advanced NLP techniques.
Solution Approach 2:
The system changes the operational parameters from manual human processing to automated computational processing. By transitioning from human cognitive resources to machine computational resources, the system maintains adaptability in handling complex natural language while dramatically reducing the cost parameter associated with processing each RFP document.
Data Source
AI summary
A system and method for extracting search terms for corresponding data elements from a natural language document identifies meaningful words within the context; identifies and structures the keywords; expounds on the keywords to optimize the search results; and captures the most relevant data elements from the corresponding database. Predetermined demographic characteristics and short (one- or two-word) search phrases that capture descriptors of behavioral characteristics are structured in the process. The result of the completed process yields a parameter set naming demographic and behavioral characteristics along with a structure that is optimized for search within a database comprising a large number of data elements.