Email Alphanumeric Candidate Classification System
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Solution Overview
Problem
Users face difficulties in remembering and retrieving alphanumeric strings from emails, such as loyalty or frequent flyer numbers, due to their varied formats and randomization, leading to inefficient searches through email messages.
Innovation Solution
A system and method that classify alphanumeric candidates from email messages by extracting email-specific, recipient-specific, and recipient-agnostic features, clustering similar candidates, and training a classifier to automatically identify and associate alphanumeric candidates with their types, allowing for prompt retrieval when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through email messages to find alphanumeric strings, then they can retrieve the information, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary classification of alphanumeric candidates by extracting features (position in email, preceding words, recipient-specific patterns, recipient-agnostic patterns) and clustering them into categories before retrieval is needed. This pre-processing creates an organized structure that enables rapid lookup without manual searching.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the raw email messages and the user's retrieval needs. The system extracts alphanumeric candidates, analyzes their contextual features, clusters them by type, and stores them in an organized manner, thereby eliminating the need for users to manually search through emails.
2Adaptability or versatility
If alphanumeric strings are presented in varied formats and randomized, then security and variety are improved, but user memorability and ease of identification deteriorate
Solution Approach 1:
The system applies local quality analysis by examining specific features of each alphanumeric candidate's context, such as its position in the email, preceding words, and recipient-specific patterns. This localized contextual analysis allows the system to identify and categorize different types of alphanumeric strings despite their varied formats, making them easily retrievable without requiring user memorization.
3Productivity
If a classification system is implemented to organize alphanumeric candidates, then retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The classification system is segmented into distinct modular components: feature extraction (position, preceding words), recipient-specific feature analysis, recipient-agnostic feature analysis, clustering algorithms, and classification model training. Each component handles a specific aspect of the classification task, making the overall system more manageable and maintainable while achieving high retrieval efficiency.
Data Source
AI summary
A technique for classifying an alphanumeric candidate in an email message can include receiving and parsing a plurality of email messages to identify at least one alphanumeric candidate. For each particular alphanumeric candidate, the technique can include: (i) associating the particular alphanumeric candidate with an originating email in which the particular alphanumeric candidate was identified, and (ii) determining email specific, recipient specific, and recipient agnostic features pertaining to the particular alphanumeric candidate. The alphanumeric candidates can be clustered based on the email specific, the recipient specific, and the recipient agnostic features to generate a plurality of clusters, with which an alphanumeric candidate type can be associated. The technique can include training an alphanumeric candidate type classifier based on the plurality of clusters and the associated alphanumeric candidate types, which can be utilized to determine the type of an unclassified alphanumeric candidate in a later received email message.