Text Editor Intent Classification for Meaning-Preserving Tone Suggestions
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Solution Overview
Problem
Writers face challenges in selecting appropriate and sincere text for common communications, such as 'Happy Birthday' greetings, 'Thank You' messages, and 'Congratulations' messages, as existing computer-based systems often alter the original meaning or lack personalization.
Innovation Solution
A computer-implemented system uses a trained multi-class text classifier to analyze the intent of the draft text and suggest personalized phrases from a large corpus, ensuring the tone and meaning are preserved while offering suggestions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rewriting systems are used to change text from one type to another, then the text tone can be transformed, but the original meaning may be changed
Solution Approach 1:
The system segments the text processing task into two distinct phases: first analyzing the semantic meaning and intent of the original text, then separately generating tone variations that preserve the identified meaning. This segmentation ensures that tone transformation does not compromise the original message.
Solution Approach 2:
The system introduces an intermediary semantic analysis layer that acts as a mediator between the original text and the rewritten versions. This intermediary step extracts and preserves the core meaning before allowing tone transformations, preventing meaning loss during the rewriting process.
2Productivity
If canned responses are provided for common communications, then writing speed is improved, but personalization and sincerity are reduced
Solution Approach 1:
The system applies local quality by providing personalized suggestions tailored to each specific context and user intent rather than using uniform canned responses. The AI analyzes the local semantic characteristics of the user's input and generates customized phrase suggestions that maintain sincerity while improving writing speed.
Solution Approach 2:
The system performs preliminary semantic analysis of the user's draft text to understand the intent and context before generating suggestions. This preliminary action enables the system to provide personalized, context-appropriate phrase suggestions that feel sincere and tailored to the specific communication situation.
3Adaptability or versatility
If a large corpus of phrases is suggested to improve personalization, then suggestion quality improves, but system complexity increases
Solution Approach 1:
The system extracts only the most relevant phrases from the large corpus based on the semantic analysis of the user's input. Rather than presenting all available phrases, the system selectively extracts and presents only those that are contextually appropriate, maintaining high suggestion quality while reducing the apparent complexity for the user.
Solution Approach 2:
The system implements feedback mechanisms that learn from user interactions with the suggested phrases. This feedback loop allows the system to refine its suggestions over time, improving quality while the learned patterns help manage complexity by focusing on the most effective phrase selections for different contexts.
Data Source
AI summary
A computer-implemented process is programmed to detect a type or category of document that is being drafted and to suggest one or more phrases or sentences to add to the original and/or substitute for the original, the suggested text being potentially more personable and sincere than the writer's original text. Suggested text phrases are selected from a large corpus of previously manually drafted sentences and phrases. Selected text phrases are ranked and filtered to result in suggesting a manageable set of text phrases. With this approach, adding specially chosen content to existing content can change the warmth or tone of the text while preserving its meaning. Unlike prior approaches, in an embodiment, the process is programmed to artificially understand the intent of the original text as a basis of suggesting other content to add. Furthermore, embodiments may interoperate with a visual or graphical user interface that is programmed to enable users to see what the change to the text will be and whether they want it before they engage with the suggestion.