Hybrid Content Curation System for Children via Algorithmic and Human Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content curation solutions for children are either inefficiently manual or inaccurately algorithmic, failing to provide a continuous age-appropriate and age-relevant content viewing experience.
Innovation Solution
A children's content system combines algorithmic content curation with human review, using classifiers to rate channels and videos for safety and relevance, and incorporates feedback from reviewers to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content curation is used for children's content, then content safety and relevance accuracy is improved, but processing efficiency and scalability deteriorate
Solution Approach 1:
The patent combines algorithmic classification systems with manual human review processes into a hybrid content curation system. The algorithmic component handles initial filtering and scoring of content based on safety and relevance criteria, while human reviewers perform detailed assessments on a subset of content, merging the speed of automation with the accuracy of human judgment to resolve the contradiction between efficiency and precision.
Solution Approach 2:
The content curation process is segmented into multiple stages: automated algorithmic pre-screening, selective human review of borderline cases, and final approval workflows. This segmentation allows the system to apply different processing methods to different content categories, maintaining high throughput for clear cases while applying rigorous manual review only where needed, thus balancing productivity and measurement precision.
2Productivity
If purely algorithmic content curation is used, then processing efficiency is improved, but content safety and relevance accuracy deteriorate
Solution Approach 1:
The system implements feedback loops where human reviewer decisions are used to retrain and refine the algorithmic classification models. Human reviewers provide corrective feedback on algorithmic misclassifications, and this feedback is incorporated into model updates, allowing the system to learn from human expertise and continuously improve accuracy while maintaining high processing efficiency through automated decision-making for routine cases.
3Measurement precision
If comprehensive human review of all content is performed, then content safety accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system applies partial human review action by having human reviewers assess only a strategically selected subset of content rather than performing comprehensive reviews of all material. The algorithm handles the majority of content through automated classification, while human reviewers focus on borderline cases, new content types, or high-risk categories, achieving high safety accuracy without the time cost of universal manual review.
Data Source
AI summary
Implementations disclose identifying content appropriate for children. A method includes selecting a list of candidate channels from a plurality of channels based on an automatically generated rating of each of the plurality of channels, filtering the list of candidate channels based on feedback received from a first group of reviewers with respect to appropriateness of content of each candidate channel for children, selecting a list of candidate videos based on an automatically generated rating of each video from every channel in the filtered list of candidate channels, and upon receiving, via a graphical user interface, a confirmation that a candidate video from the list of candidate videos is appropriate for children, setting a confirmation indicator of the candidate video to enable presentation of the candidate video to children.