Unsupervised Email Template Extraction via Sub-templates
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Solution Overview
Problem
Automated systems struggle to efficiently extract actionable information from emails with varying HTML formats without human supervision, as format changes require manual intervention and inefficiencies in supervised learning processes.
Innovation Solution
The system automatically extracts actionable information from emails in an unsupervised manner by using sub-templates to learn and adapt to minor variations in message structure, computing message templates as permutations of multiple sub-templates, and identifying core regions using domain-specific dictionaries and tree structures, allowing seamless handling of format changes without the need for new templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If supervised learning with human intervention is used to handle email format changes, then the system can adapt to new formats, but the system efficiency deteriorates due to human signaling and waiting time
Solution Approach 1:
The system performs self-learning through unsupervised template extraction and matching, automatically adapting to new email formats without human intervention. The automated template extraction process enables the system to service itself by continuously learning from incoming emails and updating its template library autonomously.
Solution Approach 2:
The system pre-extracts and stores email templates in advance, building a comprehensive template library before actual information extraction is needed. This preliminary action enables rapid matching and adaptation when new email formats arrive, eliminating the need for reactive human intervention.
2Adaptability or versatility
If human supervision is introduced to learn new templates, then the system can handle format variations, but the complexity of the process increases due to manual involvement
Solution Approach 1:
The system automatically extracts templates from emails using unsupervised learning algorithms, eliminating the need for human supervisors to manually create or update templates. The automated process handles format variations through algorithmic pattern recognition rather than manual intervention.
Solution Approach 2:
The patent replaces the mechanical process of human supervision with an automated computational system that uses unsupervised learning algorithms to extract and recognize email templates, thereby reducing process complexity while maintaining adaptability.
3Reliability
If traditional template matching is used, then the system can extract information from known formats, but it fails when email formats change
Solution Approach 1:
The system pre-extracts and stores multiple template variations in a comprehensive template library before information extraction is needed. This preliminary action ensures that when format changes occur, the system has pre-prepared templates to match against, maintaining reliable extraction accuracy.
Solution Approach 2:
The template library is dynamically updated through continuous unsupervised learning from incoming emails. The system adapts its template collection over time, allowing it to handle both known and newly emerging email formats while maintaining reliable information extraction.
Data Source
AI summary
Systems and methods are provided for extracting actionable information from emails in a completely unsupervised manner with no need for the data to be labeled (i.e., the systems and methods do not a human to identify unlabeled or relabeled emails). Changes in the email structure are automatically incorporated to learn new templates through the novel concept of sub-templates. The systems and methods incorporate the minor variations in email structure seamlessly, without needing to introduce new templates. Email templates are computed as permutations of multiple sub-templates in the email, which allows the systems and methods to handle variations in email structure seamlessly and highly efficiently. These systems and methods are extendable to any domain using structured emails, and improve the efficiency of the systems that receive and act on information contained in emails.