Semantic Misalignment Detection in Digital Content Delivery
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Solution Overview
Problem
Conventional digital content publishing systems are inaccurate, inefficient, and inflexible in aligning dynamic external digital content with digital messages, leading to misalignments that waste computing resources and require significant user interaction and processing power.
Innovation Solution
A digital misalignment system that automatically identifies and presents semantic misalignments by extracting features from digital messages and external digital content, comparing them in a semantic vector space, and notifying publishers through a graphical user interface, allowing for flexible resolution of misalignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional digital content publishing systems send digital messages with links to external digital content, then digital content can be provided to users, but the external digital content becomes outdated or incorrect over time, leading to misalignments
Solution Approach 1:
The system performs preliminary actions by extracting and storing semantic features from external digital content before sending digital messages. This pre-extraction allows the system to later compare current content with the stored features, detecting misalignments without needing to re-access the external content when users interact with messages.
Solution Approach 2:
The system implements feedback by continuously monitoring external digital content for changes and comparing it with the original semantic features. When misalignments are detected through this feedback mechanism, the system can notify users or automatically correct the content, ensuring ongoing accuracy without requiring manual verification.
2Ease of operation
If conventional systems access and provide external digital content through user interaction with digital messages, then content can be delivered, but significant computing resources and time are wasted in identifying and accessing relevant content
Solution Approach 1:
The system performs preliminary extraction of semantic features from external digital content and stores them in association with digital messages. This pre-processing eliminates the need for repeated access to external content during user interaction, significantly reducing computing resource consumption while maintaining ease of content delivery.
Solution Approach 2:
The system creates semantic feature copies of external digital content and stores them locally. These copies contain the essential semantic information needed for comparison and alignment detection, eliminating the need to repeatedly access the original external content and reducing computing resources required for content verification.
3Measurement precision
If conventional systems require navigation to digital messages and selection of digital links to identify misalignments, then misalignments can be detected, but significant user interaction and processing power are required
Solution Approach 1:
The system performs self-service by automatically extracting semantic features from external digital content and comparing them with stored features to detect misalignments. This automated process eliminates the need for manual navigation through messages and selection of links, significantly reducing the time required to identify misalignments while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary extraction and storage of semantic features from external content before misalignments occur. This pre-prepared semantic data enables rapid comparison and detection when changes happen, eliminating the need for time-consuming manual inspection and significantly reducing misalignment identification time.
4Device complexity
If conventional digital message systems rigidly provide digital messages and wait for misalignments to arise, then message delivery is simple, but the systems are inflexible in generating and providing digital messages
Solution Approach 1:
The system implements feedback by continuously monitoring external digital content for changes and comparing it with the original semantic features stored with digital messages. This feedback mechanism enables the system to automatically detect misalignments and adaptively respond by notifying users or correcting content, significantly enhancing system flexibility while maintaining the simplicity of the core message delivery process.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for determining and resolving semantic misalignments between digital messages containing links and corresponding external digital content. For example, in one or more embodiments, the disclosed systems compare semantic message features from the digital message with semantic external digital content features from the external digital content. More specifically, in at least one embodiment, the disclosed systems compare semantic message feature vectors and semantic external digital content feature vectors to determine a relevance score for the digital message and identify semantic misalignments. Additionally, in one or more embodiments, the disclosed systems provide for display a user interface that presents a plurality of digital messages, the linked external digital content, and identified semantic misalignments.