AI Email Content Extraction for Shared Inbox Routing
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Solution Overview
Problem
Manual processing of emails 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 emails, especially when attached files are involved.
Innovation Solution
A system utilizing AI models for email content extraction and classification, including correspondence classifiers and entity extractors, to determine a confidence level for routing decisions, reducing manual intervention and minimizing misclassification by applying transfer learning for model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of email messages is used, then reviewers can read and classify messages, but the process becomes time-consuming and misclassification occurs
Solution Approach 1:
The system performs preliminary classification using AI models before human reviewers process emails. The correspondence classifier and entity extractor analyze email content, attachments, and metadata in advance to determine the appropriate secondary system, reducing the time burden on reviewers while maintaining accuracy through automated pre-screening
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated AI-based classification systems. Machine learning models analyze email content, extract entities, and route messages automatically, substituting human reviewers' manual work with computational processes that are both faster and more consistent
2Reliability
If email is incorrectly classified and sent to wrong secondary system, then additional processing and storage resources are consumed, but reclassification is needed
Solution Approach 1:
The system implements feedback mechanisms where classification results are verified and refined. The AI models analyze multiple features including email content, attachments, and metadata to make informed routing decisions, and the system can learn from outcomes to improve future classification accuracy, reducing misrouting and associated resource waste
Solution Approach 2:
The system performs comprehensive preliminary analysis of email content, attachments, and metadata before routing decisions are made. This advance classification using multiple AI models ensures higher accuracy from the first attempt, preventing the need for reclassification and reducing processing resource consumption
3Reliability
If email includes attached files, then more processing and storage resources are needed, but misclassification increases rerouting
Solution Approach 1:
The system performs preliminary analysis of attachments and email content before routing. The entity extractor and correspondence classifier examine file types, content, and context in advance to determine appropriate routing, ensuring accurate classification on the first attempt and reducing the need for rerouting large files that consume significant processing and storage resources
Data Source
AI summary
A computer-implemented method includes accessing an email message received at a mail server, extracting a plurality of correspondence data from the email message, and applying a correspondence classifier to the correspondence data to determine a request type of the email message. The computer-implemented method further includes extracting 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 computer-implemented method can also include determining a confidence level of the extracting of the entities, performing 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 generating 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.