AI Email Content Extraction for Accurate Request Routing
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Solution Overview
Problem
Manual processing of email messages in shared inboxes is time-consuming and prone to misclassification, leading to increased processing, storage, and network resource demands due to incorrect routing and rerouting of messages.
Innovation Solution
A system that applies a sequence of extraction, classification, and analysis steps using artificial intelligence and machine learning to extract data from email content, ensuring high confidence in classification and reducing manual processing, thereby minimizing errant routing and rerouting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing is used to read and classify email messages, then reviewers can determine how each message should be processed, but the process becomes time-consuming and prone to misclassification
Solution Approach 1:
The system performs preliminary classification using machine learning models before human reviewers process emails. The model predicts the intended recipient or department, and reviewers only need to verify or correct the prediction, significantly reducing processing time while maintaining high accuracy through the preliminary filtering action.
Solution Approach 2:
The patent replaces the mechanical manual reading and classification process with an automated machine learning system. The ML model automatically analyzes email content, extracts features, and classifies messages, substituting human cognitive labor with computational processing that is both faster and more consistent.
2Productivity
If emails are incorrectly classified and sent to wrong secondary systems, then additional processing and storage resources are demanded, but the initial classification error causes rerouting and wasted resources
Solution Approach 1:
The system implements feedback mechanisms where classification results are monitored and evaluated. When misclassifications occur, the system learns from these errors and adjusts its model parameters. This feedback loop continuously improves classification accuracy, reducing erroneous routing and the associated waste of network and storage resources.
Solution Approach 2:
The system performs preliminary classification with high confidence thresholds before routing emails to secondary systems. By using multiple features and ensemble methods to achieve more accurate initial classification, the system prevents premature or incorrect routing decisions that would waste network bandwidth and storage resources on wrong destinations.
3Measurement precision
If a sequence of extraction, classification, and analysis steps is applied using AI and machine learning, then data extraction accuracy is enhanced, but the system complexity increases
Solution Approach 1:
The patent segments the email processing system into distinct modular components: feature extraction module, classification module, and analysis module. Each module performs a specific function and can be independently trained, optimized, and maintained. This segmentation manages system complexity by breaking down the complex AI pipeline into manageable, interchangeable units.
Solution Approach 2:
The system employs universal machine learning components that can handle multiple extraction and classification tasks. A single trained model architecture can process different types of emails, extract various features, and classify into multiple categories, reducing overall system complexity through multi-functional rather than specialized components.
Data Source
AI summary
A system includes a processing device and memory device configured to access an email message received at a mail server, extract a plurality of correspondence data from the email message, and apply a correspondence classifier to the correspondence data to determine a request type of the email message. The processing device is further configured to extract a plurality of entities from the email message in a free-form format, where extracting is performed based on determining that the request type is supported. The processing device can also determine a confidence level of the extracting of the entities, perform a lookup of the entities in one or more records of a database based on determining that the confidence level is above a confidence threshold, and generate a new processing request including prepopulated data fields populated with the entities based on identifying a match in the one or more records of the database.