Numeric Index for Contextual Data Retrieval
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Solution Overview
Problem
Current information retrieval systems lack effective numeric search capabilities, particularly in extracting and contextualizing numeric data from unstructured documents, and struggle with visualization and unit conversion, which is crucial for handling valuable internet data like financial statistics and technological specifications.
Innovation Solution
An information retrieval system utilizing a units-based index construction process for efficient numeric data extraction and contextualization, featuring automated graphical display and mathematical analysis, with an indexer module that converts number-unit pairs into standardized notation and a query module that employs scoring algorithms for relevant data retrieval, along with expert knowledge libraries for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional keyword-based search is used, then linguistic information can be retrieved, but numeric data extraction and unit conversion capabilities are lacking
Solution Approach 1:
The patent introduces a numeric index as an intermediary structure between traditional keyword indexes and numeric data. This numeric index stores pre-computed numeric features (counts, sums, averages, min, max) for each document, enabling efficient numeric queries without requiring complex real-time analysis of unstructured text. The numeric index acts as a mediator that translates unstructured numeric information into structured, queryable form.
Solution Approach 2:
The system performs preliminary extraction and storage of numeric features during the indexing phase. Instead of extracting numeric data on-demand during queries, the system pre-computes and stores numeric statistics (frequency, sum, average, min, max) for each document in the numeric index. This preliminary action enables fast numeric queries without re-processing the original unstructured text.
2Measurement precision
If full contextualization of numeric data is attempted, then retrieval accuracy improves, but processing time and complexity increase significantly
Solution Approach 1:
The patent extracts specific numeric features (count, sum, average, min, max) from unstructured documents and stores them separately in a numeric index. This extraction approach isolates numeric information from the full text, enabling efficient queries based on numeric criteria without requiring complete contextual analysis of each document during query processing.
Solution Approach 2:
The system changes the representation parameters of numeric data by storing multiple pre-computed statistics (count, sum, average, min, max) for each document. This parameter transformation allows the system to answer various numeric queries (e.g., documents with average value > X, documents containing Y occurrences) efficiently without re-processing the original text.
3Productivity
If numeric data is stored in original format, then data fidelity is maintained, but search and comparison operations become inefficient
Solution Approach 1:
The numeric index structure is designed to serve multiple query types simultaneously. It stores numeric features that enable various search operations (exact matching, range queries, statistical comparisons) without requiring separate indexes for each query type. The same numeric index supports both precise numeric searches and unit conversion operations.
Solution Approach 2:
The system creates a copied representation of numeric information in the numeric index, separate from the original document text. This copy contains pre-extracted numeric features that can be queried efficiently. The original document remains unchanged, preserving data fidelity, while the copied numeric index enables fast search operations.
Data Source
AI summary
An information retrieval and analysis system for numeric data which provides high precision and recall for numeric search and uses a methodology for determining contextualization of the extracted data. The capabilities include extracting, parsing, and contextualizing numeric data including both a numeric value and an accompanying unit. This system facilitates the organization of largely unstructured numeric data into an inverted index and other database formats. An information retrieval system which enables the exploration and refinement of an extracted numeric data set defined by a search input that may be precise or initially vague. This system also facilitates analyzing and portraying numeric data graphically, creating knowledge by combining data from multiple sources, extracting correlations between seemingly disparate variables, and recognizing numeric data trends. This system uses local natural language processing, mathematical analysis, and expert-based scientific heuristics to score the numeric and contextual relevancy of the data to the query parameters.