Interactive Machine-Generated Document Editing with Token Suggestions
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Solution Overview
Problem
Machine-generated documents, particularly source code, often require further editing to correct errors and maintain consistency, with the impact of edits not readily apparent, especially in large corpora, leading to potential functionality issues.
Innovation Solution
A computer-implemented method that includes interactive editing tools to identify user edits and suggest replacement token sequences, using a generative model to analyze and generate a new version of the document, highlighting potential related changes through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-generated documents are produced in large corpora, then productivity is improved, but the complexity of detecting and measuring edit impacts increases
Solution Approach 1:
The system segments the document into tokens and tracks edits at the token level. By comparing token sequences between versions and identifying user-replaced tokens, the system can detect edit impacts locally without analyzing the entire large corpus, thus maintaining productivity while reducing detection complexity.
Solution Approach 2:
The system introduces an intermediary comparison mechanism that automatically generates and presents suggested replacement token sequences to users. This intermediary layer handles the complex task of detecting edit impacts and generating related changes, freeing users from manual analysis of large corpora.
2Manufacturing precision
If users manually edit machine-generated documents, then manufacturing precision is improved, but the time required for editing increases
Solution Approach 1:
The system performs preliminary analysis by automatically generating suggested replacement token sequences based on detected user edits. This preliminary action identifies potential related changes before the user completes their editing task, reducing the time needed for manual review while maintaining high precision through automated analysis.
Solution Approach 2:
The system provides feedback by presenting suggested replacement token sequences to users after detecting edits. This feedback mechanism allows users to quickly review and accept or reject automated suggestions, accelerating the editing process while maintaining accuracy through the generative model's analysis.
3Reliability
If the system analyzes all possible related token changes, then reliability is improved, but device complexity increases
Solution Approach 1:
The system applies local quality analysis by focusing on tokens related to detected user edits rather than analyzing the entire document uniformly. The generative model identifies and analyzes only the local context around edited tokens to generate relevant replacement suggestions, maintaining reliability for affected areas while reducing overall system complexity.
Data Source
AI summary
Embodiments relate to interactive editing of a machine-generated document. A computer-implemented method includes receiving, by a processor, a machine-generated document and performing a comparison of a current state of the machine-generated document to a previous state. A user edit is identified as one or more user-replaced tokens of a previous token sequence based at least in part on the comparison. A new version of the machine-generated document is generated that includes the one or more user-replaced tokens and identifies one or more related tokens to replace with a suggested replacement token sequence associated with the one or more user-replaced tokens. A suggestion list is generated for display to the user in a graphical user interface to indicate the suggested replacement token sequence to replace the one or more related tokens.