Email Bot Activity Detection with LRU Caching and Rule Matching

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Solution Overview

Problem

Existing email marketing systems struggle to differentiate between human interactions and bot activities, leading to false signals and inefficient resource allocation due to the inability to accurately identify and filter out bot-generated events.

Innovation Solution

A real-time bot detection system using an LRU cache and rule-matching module to analyze email response events, applying unsupervised bot detection patterns that do not require labeled ground-truth data, enabling efficient identification and filtering of bot activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If bot detection patterns are applied to identify bot activity among email response events, then the reliability of email engagement analysis is improved, but the device complexity increases due to the need for event caches and rule-matching modules

Engineering Contradiction:

Improvereliability of email engagement analysisVSAvoiddevice complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the email processing workflow into distinct components: event generation, event caching, rule-matching for bot detection, and result application. This segmentation allows each component to be optimized independently while maintaining overall system reliability for distinguishing human from bot interactions.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Event caches serve as intermediary structures that store email response events between generation and analysis. The rule-matching module acts as an intermediary that applies bot detection patterns to cached events without requiring direct modification of the core email processing system, thereby improving reliability while managing complexity through modular design.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If bot-generated events are filtered out from email response events, then resource allocation efficiency is improved, but the loss of information increases due to potential filtering of legitimate human interactions

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidloss of information

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The rule-matching module applies multiple bot detection patterns and evaluates cached events against these patterns before filtering. This feedback mechanism allows the system to iteratively assess each event's likelihood of being bot-generated, improving resource allocation efficiency while minimizing false positives that would result in information loss.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes parameters such as timestamp analysis, activity type patterns, and event sequence characteristics to distinguish bot from human interactions. By monitoring multiple parameters simultaneously through rule-matching, the system can filter bot events to improve productivity while maintaining sensitivity to legitimate human interactions, thereby reducing information loss.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12373556B2Bot activity detection for email tracking

Publication Date: 2025.07.29 ADOBE INC

AI summary

In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.