Graymail Detection via Scoring and Remediation Modules
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Solution Overview
Problem
Conventional email filtering services struggle to manage graymail effectively, as they fail to differentiate between legitimate and unwanted bulk emails, leading to varying perceptions of value among recipients within an enterprise, and lack appropriate handling mechanisms for time-sensitive content.
Innovation Solution
A computer-implemented threat detection platform that includes a scoring module to analyze incoming emails and determine their likelihood of being graymail, and a remediation module to automatically sort such emails into appropriate folders, using machine learning models and heuristics to classify and manage graymail based on recipient preferences and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional filtering services are used to manage email, then basic spam filtering is provided, but graymail cannot be effectively differentiated from legitimate emails
Solution Approach 1:
The patent segments email classification into multiple categories (spam, graymail, legitimate) with specialized handling for each. The system divides the filtering task by creating separate classification paths and criteria for different email types, allowing graymail to be identified and routed differently from both spam and legitimate emails.
Solution Approach 2:
The patent introduces an intermediary classification layer between conventional spam filters and the inbox. This intermediary system uses additional analysis criteria (sender-recipient relationships, content patterns, timing) to identify graymail and route it to intermediate folders, acting as a mediator that prevents graymail from being misclassified as either spam or legitimate email.
2Ease of operation
If all incoming emails are routed to the inbox, then no filtering is applied, but inboxes become cluttered with unwanted messages
Solution Approach 1:
The patent extracts graymail from the main inbox stream and places it in dedicated graymail folders. This extraction process removes unwanted bulk emails from the primary view while preserving access to them through separate organizational structures, allowing users to focus on important emails while graymail remains accessible when needed.
Solution Approach 2:
The patent implements dynamic folder management where graymail folders are created, modified, and organized based on recipient preferences and behavior patterns. The system dynamically adjusts classification criteria and folder structures to optimize inbox clarity while maintaining user-specific access patterns for different types of graymail content.
3Adaptability or versatility
If manual sorting of emails is required, then user control over email organization is maximized, but time consumption increases significantly
Solution Approach 1:
The patent implements self-service automation where the system automatically classifies and routes graymail based on learned patterns and user preferences. The system serves itself by continuously analyzing email characteristics and adjusting classification rules without requiring manual user intervention, while still providing flexible organizational outcomes tailored to individual user needs.
Solution Approach 2:
The patent incorporates feedback mechanisms where user interactions with graymail (opening, deleting, moving messages) are analyzed to refine classification algorithms. This feedback loop allows the system to adapt to user preferences over time, improving accuracy and reducing the need for manual sorting while maintaining high adaptability to individual user needs.
4Object-affected harmful factors
If conventional spam filters are used, then malicious emails are blocked, but graymail is misclassified as spam
Solution Approach 1:
The patent applies different quality criteria and analysis methods to different email types. Instead of using a single uniform filtering standard, the system applies localized classification rules specific to graymail characteristics (bulk sending patterns, sender-recipient relationships, content templates) that differ from both spam and legitimate email criteria, enabling accurate differentiation.
Solution Approach 2:
The patent introduces asymmetric classification criteria where graymail evaluation uses different metrics and weights compared to conventional spam filtering. The system applies asymmetric rules that consider factors like opt-in status, sender reputation in context of recipient preferences, and content relevance, creating an imbalanced but more accurate classification approach that treats graymail differently from both spam and legitimate emails.
Data Source
AI summary
Techniques for identifying and processing graymail are disclosed. An electronic message store is accessed. A determination is made that a first message included in the electronic message store represents graymail, including by accessing a profile associated with an addressee of the first message. A remedial action is taken in response to determining that the first message represents graymail.