Cascading Neural Network for Email Reply Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing email classification systems are inefficient and prone to human errors, requiring manual drilling through layers for data access and relying on extensive hand-labeling of data, which is time-consuming and impractical for large volumes of emails, especially with limited labeled data for objection identification models.
Innovation Solution
A cascading model architecture with a Receptivity model and Objection Identification model, combined with generative text data augmentation and explainability features, to classify email replies into receptivity categories with confidence ratings, reducing the need for manual data labeling and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling is used for email classification, then model training can be performed, but it is time-consuming and impractical for large volumes of emails
Solution Approach 1:
The patent uses pre-labeled email datasets as copies or proxies for the actual target data. Instead of manually labeling all production emails, the system trains on pre-labeled datasets that replicate the labeling process, thereby avoiding repetitive manual work while maintaining classification accuracy
Solution Approach 2:
The patent performs data labeling in advance by using pre-labeled datasets before deployment. The labeling work is done preliminarily on training datasets, allowing the system to be ready for production without requiring real-time manual labeling of incoming emails
2Measurement precision
If extensive hand-labeling is performed to improve model accuracy, then classification precision increases, but the process becomes impractical for large-scale email volumes
Solution Approach 1:
The patent applies partial labeling by labeling only a subset of emails (training datasets) rather than all emails. This partial action provides sufficient training signal for the model to achieve good precision while maintaining high productivity on the full email volume
Solution Approach 2:
The system uses pre-labeled datasets as representative copies that capture the essential patterns needed for accurate classification, eliminating the need to manually label every single email in production while maintaining precision
3Device complexity
If a single comprehensive model is used for all email classification tasks, then the system is simpler, but accuracy on specific tasks like objection identification improves less
Solution Approach 1:
The patent divides the email classification task into separate specialized models: a receptivity model for determining engagement level and an objection identification model for detecting specific objections. This segmentation allows each model to specialize in its task, improving overall accuracy despite increased architectural complexity
Solution Approach 2:
The patent applies different modeling approaches to different parts of the classification task. The receptivity model handles engagement detection while the objection identification model handles specific objection types, with each part optimized for its local function, thereby improving overall system precision
4Reliability
If more labeled data is collected to improve model robustness, then model reliability increases, but data collection and labeling time increases
Solution Approach 1:
The patent uses pre-labeled datasets as synthetic copies that augment the available training data. These pre-labeled datasets serve as artificial extensions of the actual labeled data, providing robustness without requiring proportional increases in real data collection and labeling time
Data Source
AI summary
Methods and systems for a cascading model architecture for providing information on a reply email. Training sample data can be created using a user's incoming reply emails from external computer devices to a user's computer device. A receptivity neural network model can be trained using the training sample data of the reply emails, and a trained receptivity neural network model can be used to determine a receptivity classification for whether new reply emails are positive reply emails or non-positive reply emails. Sample data of non-positive reply emails can be augmented, and an objection identification neural network model can be trained on the augmented sample data of the non-positive reply emails. A trained objection identification neural network model can be used to determine a objection classification for new non-positive reply emails. Explainability information for a classified reply email can be determined to provide information key words and/or key phrases that were used by the trained receptivity neural network model and the trained objection identification neural network model to classify new positive reply emails and new non-positive reply emails.