Natural Language Tone Rewriting for Meaning Preservation
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Solution Overview
Problem
Existing natural language processing systems struggle to assist writers in aligning the tone of their text with their intended tone effectively, often leading to unintended changes in meaning or tone that can damage relationships.
Innovation Solution
A computer-implemented system that uses machine-learning models to analyze text, determine its tone, and suggest modifications to align it with the intended tone, while preserving the original meaning, using a distributed computing architecture and real-time feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ML-based techniques are used to detect tone of electronic text, then tone detection capability is improved, but the ability to preserve original meaning while improving tone is compromised
Solution Approach 1:
The system segments the text processing task into multiple specialized ML models: one model detects the current tone of the text, another model detects the intended tone, and a third model generates suggestions to bridge the gap between them. This segmentation allows each model to specialize in a specific aspect, improving overall accuracy while preserving meaning through collaborative processing.
Solution Approach 2:
The system implements feedback by providing the detected current tone and intended tone back to the user, along with specific suggestions for modification. The user can review these suggestions and accept or reject them, creating a feedback loop that ensures the original meaning is preserved while achieving the desired tone alignment.
2Adaptability or versatility
If automatic modifications are applied to align tone, then tone alignment is improved, but the risk of changing overall meaning increases
Solution Approach 1:
Instead of automatically applying full modifications to the text, the system generates partial suggestions that show only the specific portions of text that need to be changed to achieve tone alignment. The user then decides whether to accept these partial modifications, reducing the risk of unintended meaning changes while maintaining tone alignment capability.
Solution Approach 2:
The system acts as an intermediary by generating multiple candidate suggestions for tone improvement rather than directly modifying the original text. The user serves as the final decision-maker, selecting from the intermediary suggestions, which ensures that tone alignment is achieved while preserving the original meaning through human oversight.
3Productivity
If real-time tone analysis is implemented, then communication effectiveness is improved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using the same ML model architecture for multiple purposes: detecting current tone, detecting intended tone, and generating tone alignment suggestions. This universal approach reduces system complexity compared to having separate specialized systems for each function, while still providing real-time tone analysis to improve communication effectiveness.
Data Source
AI summary
In one embodiment, the disclosure provides a computer-implemented method, comprising receiving, by a computer system, electronic digital data representing a first text sequence in a first language, the first text sequence comprising unstructured natural language text; by one or more machine-learning models of the computer system, determining a score corresponding to the first text sequence, the score being determined by one or more rule-based filters and indicating one or more predicted tones; determining whether to modify the first text sequence based on the score of the first text sequence; in response to a determination to modify the first text sequence, modifying the first text sequence, the modifying comprising replacement of at least one or more words of the first text sequence to result in creating and digitally storing a second text sequence; and transmitting a graphical control element indicative of a tone associated with the second text sequence for display.