Deep Learning Communication Assistance Model for Tone Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication technologies fail to provide comprehensive solutions for detecting and addressing inappropriate, offensive, or legally problematic language and tone in written communications, which can vary significantly in context and intent.
Innovation Solution
A computer-implemented method using a machine-learned communication assistance model, specifically a long short-term memory recurrent neural network, that analyzes user input in real-time to identify problematic statements and suggests replacements, taking into account context and user feedback to adjust notification and intervention thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine-learned communication assistance model is implemented to detect problematic statements, then communication quality is improved, but device complexity increases
Solution Approach 1:
The patent introduces a machine-learned communication assistance model as an intermediary component between the user and the communication platform. This model acts as a mediator that analyzes communications for problematic statements before they are transmitted or displayed, thereby improving communication quality without requiring changes to the core communication infrastructure. The model processes communications in real-time and provides feedback to users about potentially problematic content.
2Measurement precision
If comprehensive analysis of communication content is performed to detect all types of problematic statements, then detection precision is improved, but processing time increases
Solution Approach 1:
The patent implements a detection system that analyzes communication content at multiple levels of depth. Rather than performing exhaustive analysis on every single communication, the system applies targeted analysis based on the specific context, type of communication, and risk factors present. The model can adjust the intensity and scope of analysis dynamically, performing more thorough examination only when necessary, thereby maintaining high detection precision while minimizing unnecessary processing time for low-risk communications.
3Loss of information
If the model provides detailed feedback on problematic statements, then information completeness is improved, but user experience deteriorates due to excessive notifications
Solution Approach 1:
The patent implements a feedback mechanism that adapts the level and type of notifications provided to users based on local context factors. Rather than applying a uniform notification strategy across all communications, the system adjusts feedback intensity and detail based on the specific communication context, user preferences, severity of problematic statements detected, and communication type. This allows the system to provide comprehensive information when needed while reducing notification overhead in situations where it would be excessive or unnecessary.
Data Source
AI summary
The present disclosure provides systems and methods that leverage machine-learned models (e.g., neural networks) to provide enhanced communication assistance. In particular, the systems and methods of the present disclosure can include or otherwise leverage a machine-learned communication assistance model to detect problematic statements included in a communication and/or provide suggested replacement statements to respectively replace the problematic statements. In one particular example, the communication assistance model can include a long short-term memory recurrent neural network that detects an inappropriate tone or unintended meaning within a user-composed communication and provides one or more suggested replacement statements to replace the problematic statements.