Automated Data File Editing via NLP Feedback Analysis
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Solution Overview
Problem
Manually applying reviewer feedback to data files, especially for identical or similar content, is time-consuming and error-prone, as existing software tools fail to efficiently identify and update all occurrences of specific content such as state names or proper names across a document.
Innovation Solution
A system and method that uses natural language processing to analyze reviewer feedback, determine edits, and automatically apply them to content within a data file, including identical or similar content, by parsing feedback into lexical tokens to identify noun and verb phrases, and applying edits based on these phrases across the file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual editing is used to apply reviewer feedback to all occurrences of content, then editing precision can be maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The patent replaces the manual mechanical editing process with an automated computer-based system that uses natural language processing to interpret reviewer feedback and automatically applies edits to all relevant content occurrences, eliminating the need for manual search and edit operations while maintaining editing precision through systematic rule-based or AI-driven text transformation
Solution Approach 2:
The system enables self-service editing by automatically analyzing reviewer feedback, identifying target content occurrences, and applying appropriate edits without requiring author intervention for each individual edit, allowing the document to edit itself based on processed feedback instructions
2Reliability
If manual editing is used to apply reviewer feedback consistently across identical content, then editing accuracy improves, but productivity decreases
Solution Approach 1:
The patent replaces manual editing operations with automated text processing systems that use natural language processing to interpret feedback and apply consistent edits across all occurrences of target content, ensuring uniform application of changes while dramatically increasing processing speed and productivity
Solution Approach 2:
The system provides universal editing capability that can handle various types of reviewer feedback (find-and-replace, formatting changes, content modifications) across different content types and contexts within a single automated framework, maintaining consistency across all edits while improving overall productivity
3Productivity
If automated natural language processing is used to apply reviewer feedback, then productivity and consistency improve, but system complexity increases
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between reviewer feedback and text editing operations, translating human-language instructions into structured edit commands that can be automatically executed, thereby managing system complexity through modular processing stages while maintaining high productivity
Solution Approach 2:
The system segments the editing process into distinct modules: feedback reception, natural language processing, target content identification, edit generation, and application execution. This segmentation allows each component to be developed and optimized independently, managing overall system complexity while enabling high-productivity automated editing
Data Source
AI summary
Systems and methods for editing data files. One system includes a processor. The processor is configured to receive reviewer feedback associated with a first portion of content included in a data file, analyze the reviewer feedback using natural language processing to determine an edit to the first portion of the content included in the data file, and apply the edit to the first portion of the content included in the data file. The processor is also configured to determine a second portion of the content included in the data file based on the reviewer feedback and apply the edit to the second portion of the content included in the data file.