Pattern Matching Segments for Automated Email Clustering
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Solution Overview
Problem
Existing B2C email communication templates are not easily adaptable for data extraction, and current methods require human intervention or access to the communication corpus, which can compromise privacy and security.
Innovation Solution
A method for selecting pattern matching segments to cluster electronic communications automatically, generating data extraction templates without human intervention, and applying these templates to extract nonconfidential content from subsequent communications, while ignoring confidential or fixed content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used to create data extraction templates from B2C communications, then the templates can be accurately generated, but user privacy and security are compromised due to human access to the communications
Solution Approach 1:
The patent introduces an automated template generation system that acts as an intermediary between B2C communications and data extraction templates. This system uses machine learning algorithms to analyze communication patterns and generate templates without human access to the actual communications, thereby maintaining privacy and security while achieving accurate template generation through automated pattern recognition
Solution Approach 2:
The patent replaces the mechanical process of human manual template creation with an automated computational system. The machine learning-based automated system processes communications to generate templates, eliminating the need for human reviewers to access sensitive data while maintaining or improving template accuracy through systematic analysis
2Object-affected harmful factors
If automated clustering is used to group B2C communications, then user privacy is preserved, but the ability to accurately extract data requires sophisticated pattern matching algorithms
Solution Approach 1:
The patent segments the automated template generation process into distinct components: communication clustering based on metadata, pattern matching segment identification, and template generation. This segmentation allows the system to process communications in manageable stages using automated algorithms, reducing the need for complex end-to-end processing while maintaining privacy through automated operation
Solution Approach 2:
The patent performs preliminary clustering of B2C communications based on metadata before template generation. This preliminary organization groups communications by sender, recipient, or other identifying information, enabling the subsequent automated template generation to work with pre-organized data sets and reducing the computational complexity of the overall system
3Measurement precision
If pattern matching segments are selected based on coverage measures, then clustering accuracy improves, but the computational time and resources increase
Solution Approach 1:
The patent changes the parameters used for pattern matching segment selection by incorporating coverage measures that evaluate how well segments represent entire clusters. By optimizing segment selection based on coverage rather than exhaustive analysis, the system achieves accurate clustering while reducing computational time through smarter parameter choices
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for selecting pattern matching segments suitable for electronic communication clustering. A set of pattern matching segments may be identified that match at least one of a corpus of electronic communication addresses. A measure of coverage of each of the set of pattern matching segments across the corpus of electronic communication addresses may be determined. A score associated with each pattern matching segment may be determined based on the measure of coverage and one or more measures of flexibility associated with each of the set of pattern matching segments. One or more of the pattern matching segments may be selected based on the determine scores. A corpus of electronic communications may then be grouped into a plurality of clusters based on a comparison of the one or more selected pattern matching segments to electronic communication addresses associated with the corpus of electronic communications.