Personalized Gray Spam Predictor for Email Filtering
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Solution Overview
Problem
Users face difficulties in managing 'gray spam' emails, which are subjectively considered unwanted but not malicious, as existing filtering methods either block legitimate senders or fail to address user preferences, leading to frustration and erroneous spam votes.
Innovation Solution
A personalized gray spam predictor is developed using user behavior data to identify and filter out 'gray spam' messages, allowing for their separation from wanted emails and preventing legitimate senders from being mislabeled as spam, while learning from user feedback to enhance email management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If messages are blocked based on sender or domain, then known spam is filtered out, but legitimate senders are incorrectly blocked
Solution Approach 1:
The patent applies local quality by transitioning from global blocking (blocking entire domains or senders) to localized filtering (filtering only specific messages based on individual user preferences and behavior patterns). The system analyzes message content, sender behavior, and user interactions to determine on a per-message basis whether to filter, allowing legitimate senders to be distinguished from spammy ones through personalized evaluation rather than blanket blocking.
Solution Approach 2:
The patent introduces an intermediary mechanism - a personalized predictor model - that sits between the incoming message and the user's inbox. This mediator evaluates each message through the lens of individual user preferences and historical behavior, acting as a filter that can block unwanted messages while preserving legitimate communications. The predictor model serves as an intelligent intermediary that resolves the contradiction by making nuanced, context-aware filtering decisions.
2Adaptability or versatility
If gray spam is not filtered, then legitimate senders are not blocked, but unwanted messages accumulate in the inbox
Solution Approach 1:
The patent implements self-service by enabling the system to automatically learn and adapt to each user's unique preferences through observation of their interaction patterns. The personalized predictor model continuously refines its understanding of what constitutes unwanted versus wanted messages for each user, eliminating the need for manual configuration or explicit user input. The system serves itself by autonomously adjusting filtering behavior based on accumulated user behavior data.
Solution Approach 2:
The patent leverages feedback mechanisms where user interactions with messages (reading, deleting, marking as spam, ignoring) are fed back into the personalized predictor model. This feedback loop allows the system to continuously improve its accuracy in identifying gray spam for each user. The model learns from actual user behavior patterns, progressively becoming better at predicting which messages users want to receive and which they want to filter out.
3Reliability
If spam votes are collected from all users, then spam detection improves, but legitimate senders are mislabeled as spam
Solution Approach 1:
The patent applies local quality to spam voting by transitioning from uniform spam detection (treating all users equally) to personalized spam detection (tailoring spam identification to individual user preferences). The system evaluates whether a message constitutes spam separately for each user based on their unique behavior patterns and preferences, preventing erroneous spam votes from users whose subjective dislike of a message type does not reflect actual spam conditions.
Solution Approach 2:
The personalized predictor model serves as an intermediary that filters and validates spam votes before they are processed by the global spam detection system. This mediator evaluates each potential spam vote through the lens of individual user preferences and behavior, determining whether the user's reaction truly indicates spam or merely reflects subjective dislike. The predictor model thus protects the spam detection system from being contaminated by erroneous votes.
Data Source
AI summary
As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.