AI Editing Interface for Transcript Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine Learning Models (MLMs) used in Natural Language Processing (NLP) for converting spoken utterances to written transcripts often require human editing due to inaccuracies in identifying words, especially in domains with jargon and similar-sounding terms, and existing editing tools do not provide adequate assistance for sensitive tasks.
Innovation Solution
The development of User Interfaces (UIs) that utilize MLM confidence rankings and contextual evidence within transcripts to suggest alternative phrases, allowing annotators to correct and refine outputs, and regenerate summaries based on edited text, while maintaining data privacy by not learning from prior text entry history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Machine Learning Models are used to automatically convert spoken utterances to written transcripts, then productivity is improved, but manufacturing precision deteriorates due to inaccuracies in identifying words
Solution Approach 1:
The patent introduces an intermediary editing interface between the automated transcription system and the final output. This interface allows human annotators to review, correct, and refine MLM-generated transcripts and summaries. The system presents candidate corrections based on confidence rankings and contextual evidence, enabling humans to act as mediators who resolve ambiguities that automated systems cannot handle, thereby improving transcription accuracy while maintaining the efficiency benefits of automated processing.
2Ease of operation
If traditional editing tools are used for transcript editing, then ease of operation is maintained, but manufacturing precision deteriorates because these tools cannot provide adequate assistance for sensitive tasks
Solution Approach 1:
The system enables self-service editing by allowing the MLM to generate its own candidate corrections based on confidence rankings and contextual evidence from the transcript. The editing interface automatically presents the most likely corrections to annotators, reducing the need for manual intervention while maintaining high accuracy. The system serves itself by using its own output (confidence scores, contextual patterns) to generate editing suggestions, thereby improving precision without sacrificing ease of operation.
3Manufacturing precision
If MLM confidence rankings are used to suggest alternative phrases, then manufacturing precision is improved, but device complexity increases due to the need for additional UI components
Solution Approach 1:
The patent segments the editing interface into distinct functional components: a confidence ranking display showing multiple candidate corrections, a contextual evidence section presenting supporting information from the transcript, and a selection interface for annotators. This segmentation allows each component to handle a specific aspect of the editing task independently, improving precision through comprehensive information presentation while managing complexity through modular design. The segmented approach enables annotators to review evidence systematically without being overwhelmed by a monolithic complex interface.
4Manufacturing precision
If contextual evidence from transcripts is used for suggestions, then manufacturing precision is improved, but loss of information increases due to privacy restrictions preventing learning from prior text entry history
Solution Approach 1:
The system performs preliminary action by extracting and analyzing contextual evidence directly from the current transcript before generating suggestions. Instead of relying on historical learning from prior text entries (which is restricted by privacy concerns), the system proactively identifies patterns, co-occurrences, and contextual relationships within the current document. This preliminary analysis of available contextual information enables accurate suggestions while respecting privacy restrictions, as all learning is performed in-memory on the current dataset without persistent storage or cross-document learning.
Data Source
AI summary
Artificial intelligence assisted editing may be provided by providing, via a Graphical User Interface (GUI), a transcript of a natural language conversation and a summary of the natural language conversation based on the transcript generated by a machine learning model; in response to receiving user selection of a selected phrase in the transcript or summary, providing an edit interface in the GUI; querying the machine learning model for a suggested phrase to replace the selected phrase with; and populating the edit interface with the suggested phrase.