Misalignment Detection for Digital Messages
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Solution Overview
Problem
Conventional digital content publishing systems are inefficient in identifying and correcting misalignments between digital messages and external digital content, often requiring excessive time and user interaction to check for inaccuracies, as links to external content can become outdated or incorrect, leading to errors and inefficiencies in digital content distribution.
Innovation Solution
A misalignment identification system that uses a machine learning model to analyze features extracted from digital messages and external digital content, identifying misalignment classes and presenting them via a graphical user interface, allowing for automatic detection and correction of misalignments, and receiving publisher feedback for model personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional digital content publishing systems manually check each link in digital messages, then misalignment detection capability is achieved, but time consumption and user interaction requirements increase excessively
Solution Approach 1:
The patent replaces manual mechanical checking of links with an automated machine learning-based system. The system uses trained models to automatically detect misalignments between digital messages and external digital content, eliminating the need for publishers to manually click and analyze each link. This substitution dramatically reduces time consumption while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-service by automatically monitoring and detecting misalignments without requiring continuous human intervention. The machine learning model continuously analyzes digital content and messages, automatically identifying misalignments and alerting publishers, thereby making the detection process autonomous and eliminating repetitive manual checking.
2Measurement precision
If conventional systems require publishers to individually analyze each link, then accurate misalignment identification is achieved, but user interaction and computational resources are excessively consumed
Solution Approach 1:
The patent replaces complex manual analysis processes with an automated machine learning system. The system handles all aspects of misalignment identification automatically, from analyzing digital messages and external content to comparing them and detecting misalignments. This eliminates the need for publishers to engage in complex individual link analysis, reducing user interaction complexity while maintaining identification accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary between digital messages and external digital content. It automatically performs the comparison and analysis that would otherwise require direct human intervention, simplifying the user experience by handling the complex analysis in the background and presenting only the results to publishers.
3Reliability
If manual link checking is performed periodically, then misalignment detection is achieved, but productivity and efficiency of digital content distribution are reduced
Solution Approach 1:
The patent implements continuous automated monitoring of digital content and messages. The machine learning system operates continuously in the background, constantly analyzing new content and messages for misalignments without interruption to the digital content distribution process. This continuous operation maintains link accuracy while having minimal impact on productivity, as the monitoring occurs asynchronously.
Solution Approach 2:
The system replaces periodic manual checking with continuous automated monitoring. The machine learning model continuously scans and analyzes digital content and messages, detecting misalignments in real-time or near-real-time. This eliminates the need to pause distribution workflows for manual checks, maintaining both reliability and high productivity.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for determining and resolving misalignments between digital messages containing links and corresponding external digital content. For example, in one or more embodiments, the disclosed systems extract a plurality of alignment classification features from a digital link in a digital message and corresponding external digital content. Based on the alignment classification features and using a machine learning classification model, the disclosed system can generate alignment probability scores for a plurality of misalignment classes. The disclosed system can report identified misalignments of corresponding misalignment classes in a misalignment identification user interface. Furthermore, the disclosed system can receive publisher input via the misalignment identification user interface to further personalize the machine learning classification model.