Latent Feature Tag Routing via Confidence Metrics
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Solution Overview
Problem
Existing data management systems face challenges in efficiently and accurately tagging large collections of items, as both manual and automated tagging methods are resource-intensive and prone to errors, especially when dealing with new or unknown items, leading to inaccurate or irrelevant tags.
Innovation Solution
The implementation of a latent feature model, such as a recurrent neural network language model, to identify subjective contextual information and behavioral data, allowing for the grouping and classification of items based on similarity, and the use of confidence metrics to determine the appropriate tagging route, whether manual, automated, or quality control, to ensure accurate tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used for large data collections, then tagging accuracy is improved, but resource consumption and time required increase significantly
Solution Approach 1:
The patent segments the tagging process into multiple routing paths (automated tagging, manual tagging, quality control) based on item characteristics and confidence scores. This allows high-confidence items to be processed quickly through automated paths while only low-confidence items receive manual attention, resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent introduces a confidence scoring mechanism as an intermediary that evaluates automated tagging results and determines routing decisions. This intermediary layer enables the system to automatically filter out high-quality tags that don't require manual review, reducing time loss while maintaining accuracy through selective human verification.
2Productivity
If automated tagging is used for all items, then processing speed is improved, but tagging accuracy deteriorates due to errors with new or unknown items
Solution Approach 1:
The patent implements dynamic routing that adapts the tagging approach based on item characteristics, confidence scores, and historical performance data. The system dynamically adjusts between automated and manual tagging pathways, enabling high processing speed for reliable cases while maintaining accuracy through adaptive human review for uncertain cases.
Solution Approach 2:
The patent incorporates feedback loops where tagging results (both automated and manual) are evaluated and used to update confidence scores and routing decisions. This feedback mechanism allows the system to learn from past performance, improving reliability over time while maintaining high processing speed through increasingly accurate automated predictions.
3Loss of information
If extensive manual tagging is performed on all items, then tagging completeness is improved, but resource efficiency deteriorates due to limited human resources
Solution Approach 1:
The patent applies partial action by performing manual tagging only on a subset of items that fall below confidence thresholds, rather than exhaustively tagging all items manually. This selective approach ensures sufficient tagging completeness for high-value items while dramatically improving resource efficiency by avoiding unnecessary manual effort on high-confidence automated tags.
Solution Approach 2:
The patent applies different quality levels of tagging to different items based on their characteristics and importance. High-confidence items receive automated tagging with lower resource investment, while low-confidence or high-value items receive thorough manual tagging. This local differentiation of quality ensures adequate completeness where needed while optimizing overall resource efficiency.
4Productivity
If confidence metrics are used to route items, then resource allocation is improved, but system complexity increases due to additional routing logic
Solution Approach 1:
The patent implements a universal routing framework that handles multiple tagging scenarios (automated, manual, quality control) through a single confidence-score-based decision system. This multi-functional approach consolidates what could be multiple separate systems into one unified routing mechanism, improving resource allocation efficiency while limiting the growth of system complexity through standardized processing logic.
Data Source
AI summary
Features are disclosed for identifying and routing items for tagging using a latent feature model, such as a recurrent neural network language model (RNNLM). The model may be trained to identify latent features for catalog items such as movies, books, food items, beverages, and the like. Based on similarities in latent features, tags previous assigned to items may be applied to untagged items. Application may be manual or automatic. In either case, resources need to be balances to ensure efficient tagging of items. The included features help to identify and direct these limited tagging resources.