Cookie Categorization via Machine Learning Ensembling
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Solution Overview
Problem
Manual categorization of cookies is tedious and challenging due to the need for encoding specific patterns in cookie names, making it inefficient for large-scale categorization and compliance with privacy regulations.
Innovation Solution
A system and method utilizing a processing subsystem with a machine learning module to convert complex cookie features into discrete features, build classifiers, and predict categorization through ensembling learning, merging results with manually categorized cookies for large-scale cookie categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization methods are used with pattern encoding rules, then categorization accuracy can be maintained, but the process becomes tedious and inefficient for large-scale cookies
Solution Approach 1:
The patent replaces manual mechanical categorization with an automated machine learning system. The ML model processes cookie features (names, domains, paths, attributes) to automatically assign categories, eliminating the need for manual pattern encoding while maintaining high accuracy through trained classification algorithms.
Solution Approach 2:
The system enables self-service categorization where the ML model learns from training data and autonomously categorizes cookies without human intervention. The model continuously improves by learning from labeled examples, allowing the system to serve itself in terms of categorization decisions.
2Productivity
If automated machine learning methods are used, then productivity and scalability improve, but system complexity increases
Solution Approach 1:
The patent segments the categorization system into distinct modular components: feature extraction module, ML model training module, prediction module, and category assignment module. Each module handles a specific aspect of the process, making the overall complex system manageable and maintainable through clear separation of concerns.
3Measurement precision
If comprehensive cookie features are analyzed, then categorization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary feature extraction and data preprocessing before the actual categorization process. By pre-processing cookie data to extract relevant features (name, domain, path, attributes) and prepare training datasets in advance, the system reduces processing time during deployment while maintaining comprehensive analysis for accuracy.
Data Source
AI summary
A system and method for large scale categorization of website cookies is disclosed. The method includes gathering information about cookies from a first and second source. The cookies include complex and discrete features. The method includes populating the cookies into a first and second table. The method includes subjecting the first and second table to a machine learning technique to recognize and determine the features. The machine learning technique is operable to convert the complex features into discrete features, wherein the discrete features are set by using at least one of external datasets and embedding the complex features; embed the cookies, wherein a classifier is built as an output of embedding of the cookies; and create a model by using ensembling learning. The method includes categorizing the cookies into a third table and a fourth table. The method includes merging the third and fourth table.