Automated Online Ad Suitability Classification via Machine Learning
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Solution Overview
Problem
The manual process of determining whether online advertisements are suitable for publication on web pages is time-consuming and prone to errors, especially for fast-growing websites that struggle to keep pace with the rapid generation of new content.
Innovation Solution
A computer-executable method using a trained machine learning system to analyze web page articles and advertisements, generating numeric likelihoods to determine suitability for accompaniment based on predefined categories and threshold values, thereby automating the decision to publish or exclude advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human administrators manually review and determine advertisement suitability, then accuracy and control are improved, but time consumption and productivity are worsened
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system. The machine learning model automatically analyzes web page content and determines advertisement suitability, eliminating the need for human administrators to manually review each advertisement placement opportunity, thus resolving the contradiction between manual accuracy and automated speed
Solution Approach 2:
The system enables self-service automation where the machine learning model independently evaluates and decides on advertisement publication without human intervention. The model processes web page content, generates suitability determinations, and executes advertisement placement decisions autonomously, transforming a manual service into an automated self-service system
2Ease of operation
If human administrators manually manage advertisement publication, then control and judgment are improved, but time consumption is worsened
Solution Approach 1:
The patent substitutes the manual control mechanism with an automated machine learning system that makes advertisement suitability determinations. The system processes web page content, applies learned patterns and rules, and automatically executes placement decisions, replacing human time investment with automated computational processing
3Productivity
If websites grow rapidly with new content generation, then productivity and scalability are improved, but the ability to maintain manual advertisement management is worsened
Solution Approach 1:
The machine learning system provides self-service automation that scales with website growth. The model automatically processes new web pages and content as they are generated, maintaining consistent advertisement management across expanding content volumes without requiring proportional increases in human management complexity
Solution Approach 2:
The system transforms the management approach from manual human processing to automated computational processing. By changing the fundamental parameter of processing capability from human to machine, the system can handle rapid content generation and large-scale advertisement management that would be impossible with manual processes
Data Source
AI summary
Exemplary embodiments provide systems, devices, one or more non-transitory computer-readable media and computer-executable methods for managing publication of online advertising. In exemplary embodiments, computer-based publication techniques may include, but is not limited to, automatically determining whether the content of a particular web page article is suitable or unsuitable for accompaniment with one or more advertisements, automatically determining whether an advertisement is suitable or unsuitable for publication on a web page associated with a web page article, and automatically determining a category that may be used to classify the content of a web page article in order to select one or more categories of advertisements suitable for accompaniment with the web page article.