Communication Modification to Reduce ML Suppression Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current artificial intelligence-based communication suppression systems are prone to inaccuracies, leading to loss of valuable information or vulnerability to malicious communications due to insufficient criteria for determining suppression, making it difficult to identify and correct errors in machine learning model results.
Innovation Solution
A communication processing machine learning model is trained to predict whether a candidate communication will be suppressed by a user machine learning model, generating different instances to determine the likelihood of suppression and updating the communication to minimize suppression, thereby improving accuracy and reducing false negatives or false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used for communication suppression, then automation and processing speed are improved, but accuracy and reliability deteriorate due to insufficient criteria and opaque decision-making processes
Solution Approach 1:
The patent implements feedback mechanisms where communication processing models receive feedback about suppression decisions and their outcomes. This feedback loop allows the system to learn from past decisions and improve its accuracy over time, addressing the reliability issue while maintaining automation benefits
Solution Approach 2:
The patent segments the communication suppression process into multiple independent components: detection models, processing models, and evaluation models. Each component can be trained and optimized separately, improving overall system reliability while maintaining high automation levels
2Speed
If machine learning models are used for communication suppression, then processing speed is improved, but measurement precision deteriorates due to insufficient criteria for determination
Solution Approach 1:
The patent introduces additional evaluation dimensions beyond traditional suppression criteria. By evaluating communications across multiple dimensions (content, context, user behavior patterns, temporal patterns), the system achieves both high processing speed through automated multi-dimensional analysis and improved prediction precision
Solution Approach 2:
The patent dynamically adjusts model parameters and evaluation criteria based on incoming communication data and system performance metrics. This allows the system to optimize processing speed by adjusting parameters in real-time while maintaining high precision through adaptive parameter changes
3Productivity
If machine learning models are used for communication suppression, then productivity is improved, but loss of information increases due to inaccurate suppressions
Solution Approach 1:
The patent implements prior cushioning by creating buffer mechanisms that allow for review and correction of suppression decisions. The system maintains logs and evaluation capabilities that can compensate for inaccurate suppressions by enabling recovery of wrongly suppressed communications while maintaining high productivity
Solution Approach 2:
The patent introduces intermediary evaluation models that act as mediators between the primary suppression models and the final communication decisions. These intermediary models review and validate suppression decisions, reducing information loss while maintaining overall system productivity through automated mediation
4Extent of automation
If machine learning models are used for communication suppression, then automation is improved, but device complexity increases due to specialized knowledge requirements
Solution Approach 1:
The patent designs communication processing models with multi-functionality, where a single model architecture can perform multiple tasks (detection, processing, evaluation) by adjusting parameters and training data. This universal approach reduces overall system complexity while maintaining high automation capabilities
Solution Approach 2:
The patent uses simplified copy models that replicate the functionality of complex machine learning models but with reduced computational requirements. These copy models can be used for preliminary processing and evaluation, reducing the complexity burden on the main system while maintaining automation
Data Source
AI summary
Methods and systems are described herein for generating communication modifications to reduce inaccurate machine-learning-based communication suppressions. The system may receive a candidate communication to be sent to a user device or user account. The system may generate a prediction indicating whether a negative action is likely to be taken by a machine learning model with respect to the candidate communication. Based on a prediction that a negative action is likely to be taken by a machine learning model with respect to the candidate communication, the system may modify the candidate communication.