Spreadsheet Template Inference for Non-Contiguous Data Extraction
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Solution Overview
Problem
Conventional spreadsheet data analysis tools struggle to analyze data effectively when it is organized in non-contiguous formats, requiring restructuring and reformatting, which is resource-intensive and often inaccurate.
Innovation Solution
A method for automatically extracting data from spreadsheets, regardless of organization, by identifying characteristics of cells, determining template types, and generating a standardized output table using a column template evaluation node network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional spreadsheet data analysis tools are used on non-contiguous data formats, then data analysis can be performed, but the process requires resource-intensive restructuring and reformatting that reduces productivity and increases complexity
Solution Approach 1:
The patent segments the spreadsheet data extraction process into distinct template types (categorical template and detailed record template), allowing different extraction strategies for different data patterns. This segmentation enables the system to handle non-contiguous data formats without requiring complete restructuring, as each template can independently process its designated data type.
Solution Approach 2:
The patent creates a universal data extraction system that can handle multiple spreadsheet formats and organizational structures through a single automated platform. The system uses machine learning models that can adapt to various data layouts (single-table, multi-table, non-contiguous formats) without requiring separate tools or manual reformatting processes for each format type.
2Adaptability or versatility
If data is organized in non-contiguous formats in spreadsheets, then data can be stored flexibly, but conventional analysis tools cannot readily analyze the data without reformatting which reduces measurement precision
Solution Approach 1:
The patent changes the parameters of data extraction by using machine learning models that can dynamically adjust to different spreadsheet organizational parameters. Instead of requiring data to conform to fixed structural parameters, the system adapts its extraction parameters based on the actual data layout, maintaining both flexibility and precision simultaneously.
Solution Approach 2:
The patent replaces mechanical manual reformatting processes with an automated machine learning-based extraction system. This substitution eliminates the need for manual data restructuring while maintaining high extraction accuracy, as the automated system can interpret and process non-contiguous formats directly without human intervention.
3Ease of operation
If manual restructuring of spreadsheet data is performed to make it compatible with analysis tools, then data can be analyzed, but the process is time-consuming and reduces productivity
Solution Approach 1:
The patent implements a self-service data extraction system where the machine learning models automatically perform the work of data interpretation and extraction without requiring manual intervention. The system serves itself by autonomously identifying data patterns, selecting appropriate templates, and extracting information from non-contiguous formats, eliminating the need for manual restructuring operations.
Solution Approach 2:
The patent performs preliminary data extraction and template matching actions automatically before the user needs to analyze the data. By pre-processing the spreadsheet data through automated template identification and extraction, the system prepares the data for analysis in advance, eliminating the need for time-consuming manual restructuring operations when analysis is needed.
Data Source
AI summary
Described are methods for automatically extracting data from structured documents e.g. spreadsheets, regardless of the manner in which data is organized, and using the extracted data to generate an output table that is in a standardized format. The method can include the operations for automatically extracting data from a spreadsheet that defines rows and columns and includes a plurality of cells that are delineated by the rows and the columns, by identifying characteristics of data included in each cell of the column, determining a template type of the column based on the characteristics of the data in each selected cell of the column, and determining, from among a plurality of cells of the column and based on characteristics of the data included in the plurality of cells of the column, a representative cell that is representative of the determined template type of the column.