Support Vector Machine Classifies Resource Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Many resources on the web do not provide change notifications for updates, making it difficult for users to track significant changes without relying on web feeds, which are not universally supported.
Innovation Solution
A method using a support vector machine (SVM) to classify changes in resources by analyzing feature vectors derived from content, structure, and traffic data, determining whether a change notification is warranted, and generating notifications for significant changes even if the resource does not support web feeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If change notifications are provided for all resource updates, then users receive complete information about all changes, but users are overwhelmed with non-important changes such as copyright notice updates
Solution Approach 1:
The patent applies parameter changes by transforming the classification problem into a mathematical space where resources are represented as vectors of features. A support vector machine learns optimal parameters (hyperplane) to separate significant from insignificant changes, enabling automated filtering without manual evaluation of each change type.
Solution Approach 2:
The system enables self-service by allowing resources to be automatically classified based on their inherent features. The support vector machine model autonomously determines which changes warrant notifications by analyzing resource characteristics, eliminating the need for manual configuration or human judgment for each resource.
2Measurement precision
If manual evaluation is used to determine significant changes, then notification accuracy is high, but the process is subjective and cannot be scaled to thousands of resources
Solution Approach 1:
The patent substitutes the mechanical system of manual human evaluation with an automated computational system. The support vector machine replaces subjective human judgment with objective mathematical classification, enabling consistent and scalable decision-making across thousands of resources while maintaining high accuracy through learned patterns from training data.
Solution Approach 2:
The system creates a computational model that copies and generalizes the decision-making process. By training the support vector machine on examples of significant and insignificant changes, the model learns to replicate human judgment patterns at scale, applying the same classification logic consistently across all resources without requiring actual human evaluators for each case.
3Ease of operation
If web feeds are used to provide change notifications, then updates are provided for resources that support them, but many resources do not provide web feeds making this approach inapplicable
Solution Approach 1:
The patent applies universality by creating a notification system that works for all resources regardless of whether they support web feeds. The support vector machine-based classifier provides a universal mechanism that can evaluate any resource type, making the notification system adaptable to diverse resource formats and eliminating the limitation of feed-dependent approaches.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying changes to resources. In one aspect, a method includes obtaining a first version of a resource. The method includes obtaining a second version of the resource, the second version representing a change to the first version of the resource. The method includes determining feature data that describes changes from the first version to the second version. The method includes creating a feature vector from the feature data. The method includes providing the feature vector to a support vector machine, the support vector machine trained to classify the second version of the resource as having one of changes requiring a change notification or changes not requiring a change notification. The method includes classifying the second version as requiring a change notification or not requiring a change notification based on output from the support vector machine.