Smart Spreadsheet Columns Using NLP for Automated Data Population
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Solution Overview
Problem
Existing spreadsheet software relies on laborious manual data entry or macros to automate data filling, which is inefficient and limited in dynamic data updates, whereas recent advancements in natural language processing (NLP) and deep learning algorithms have not been effectively integrated for automated data population in spreadsheets.
Innovation Solution
The integration of a machine learning module using NLP software, such as BERT and Question Answering systems, to create 'smart columns' in spreadsheets that automatically populate cells with answers to template questions containing variables from adjacent columns, updating dynamically upon changes in the variable columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to populate spreadsheet cells, then data can be added to spreadsheets, but the process is laborious and inefficient
Solution Approach 1:
The system enables self-service automation where the spreadsheet automatically generates questions from column headers and variables, retrieves answers through NLP queries, and populates cells without manual intervention. The automated macro performs data population tasks independently, eliminating the need for users to manually enter data while maintaining accuracy and efficiency.
Solution Approach 2:
The patent replaces manual mechanical data entry operations with an automated system combining macro programming and natural language processing. Instead of users physically typing data, the system uses NLP software to generate queries from column variables, automatically retrieves information, and fills cells, substituting human labor with intelligent automation.
2Extent of automation
If macros are used to automate data filling, then particular tasks can be automated, but the system is limited in dynamic data updates and sophistication
Solution Approach 1:
The system dynamically changes parameters by automatically generating different questions based on column headers and variables. Instead of using fixed macro instructions, the NLP software transforms column metadata into adaptive queries, allowing the automation to respond to different data structures and update dynamically when column definitions change.
Solution Approach 2:
The patent introduces NLP software as an intermediary between the macro system and the data population process. This intermediary layer translates column variables into natural language queries, enabling the macro to access and retrieve information from various sources adaptively, thereby enhancing the system's versatility and dynamic update capabilities.
3Adaptability or versatility
If NLP software is integrated to create smart columns, then sophisticated automation and dynamic updates are achieved, but the system complexity increases
Solution Approach 1:
The patent segments the complex NLP-based automation system into distinct functional components: macro programming for task orchestration, NLP software for query generation, and spreadsheet integration for data management. This segmentation allows each component to operate independently with well-defined interfaces, managing complexity while maintaining sophisticated automation capabilities.
Solution Approach 2:
The system achieves universality by creating a multi-functional framework where the NLP software can generate queries from any column variables, retrieve information from multiple sources, and populate various cell types. This universal approach handles diverse data population tasks through a single integrated system, managing complexity through standardized processes.
Data Source
AI summary
Presented herein are systems and methods for populating electronic documents, and, in particular, automatically filling columns in a spreadsheet, using a machine learning module. In certain embodiments, the machine learning module comprises natural language processing (NLP) software, for example, Question Answer (QA) software.