Smart Content Recommendation Tool for Authors
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Solution Overview
Problem
Content authors face difficulties in efficiently locating and embedding relevant images, links, and other media within their authored content due to the manual and time-consuming process of searching, ensuring safety, and authorization, which hampers the content creation process.
Innovation Solution
An AI-driven smart digital content recommendation tool that analyzes input text and images, extracts keywords, converts content into vectors, and compares them to a content repository to recommend relevant media items, which can be easily embedded into the authoring interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual searching and verification methods are used to locate and embed relevant media content, then content authors can ensure safety and authorization of embedded items, but the process becomes time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary actions by pre-organizing media content in repositories with metadata tagging and pre-verifying authorization status. When an author needs content, the system has already prepared filtered results based on the author's permissions and content requirements, eliminating the need for manual safety checks during the creation process.
Solution Approach 2:
The patent introduces an intermediary system (media recommendation tool) that sits between the content repository and the author. This intermediary automatically filters, tags, and verifies media content based on authorization rules and content relevance, allowing authors to receive pre-validated recommendations without manual verification while maintaining reliability.
2Reliability
If comprehensive manual verification of media content is performed to ensure safety and authorization, then content quality and security are maintained, but the complexity of the content creation process increases
Solution Approach 1:
The system implements self-service by enabling media content to be automatically tagged and categorized based on its inherent properties and metadata. The content repository serves itself by organizing and filtering content according to pre-defined authorization rules and relevance criteria, eliminating the need for complex manual verification processes while maintaining content safety.
Solution Approach 2:
An intermediary recommendation engine automatically handles the verification and filtering of media content by analyzing authorization metadata and content relevance. This intermediary layer absorbs the complexity of safety verification, presenting simplified results to authors without exposing the underlying complex verification processes.
3Ease of operation
If authors manually locate and embed each media item individually, then precise control over content selection is achieved, but the time required for content creation increases significantly
Solution Approach 1:
The system performs preliminary filtering and organization of media content based on authorization rules and content relevance before the author needs it. Media items are pre-tagged with metadata and organized in repositories, so when an author searches for content, results are already filtered and ranked, dramatically reducing the time to locate suitable media while maintaining selection control.
Solution Approach 2:
The recommendation system provides feedback to authors by presenting ranked media suggestions based on their content needs and authorization levels. Authors can review these feedback results and select from pre-filtered options, combining automated time-saving filtering with manual control over final selection. The system learns from author choices to improve future recommendations.
Data Source
AI summary
Techniques describes herein include using software tools and feature vector comparisons to analyze and recommend images, text content, and other relevant media content from a content repository. A digital content recommendation tool may communicate with a number of back-end services and content repositories to analyze text and/or visual input, extract keywords or topics from the input, classify and tag the input content, and store the classified/tagged content in one or more content repositories. Input text and/or input images may be converted into vectors within a multi-dimensional vector space, and compared to a plurality of feature vectors within a vector space to identify relevant content items within a content repository. Such comparisons may include exhaustive deep searches and/or efficient tag-based filtered searches. Relevant content items (e.g., images, audio and/or video clips, links to related articles, etc.), may be retrieved and presented to a content author and embedded within original authored content.