Adaptive Concept Learning for Digital Media Retrieval
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Solution Overview
Problem
Existing digital media retrieval systems fail to meet user expectations by not effectively learning and retrieving content relevant to specific user criteria, despite using sophisticated pattern recognition systems.
Innovation Solution
A system and method that learns new trained concepts by obtaining digital media items, receiving feedback on positive and negative examples, and using machine learning representations to determine a trained concept for retrieving relevant content, enabling improved content retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed pattern recognition systems are used to retrieve digital media items, then the system structure remains simple and stable, but the system fails to meet user expectations and cannot learn new concepts
Solution Approach 1:
The system implements feedback mechanisms where user interactions (positive and negative examples) are fed back into the machine learning model to continuously improve and adapt the trained concepts. This allows the system to learn new concepts dynamically while maintaining a structured architecture through the use of feedback loops in the learning process.
Solution Approach 2:
The patent transforms the static fixed pattern recognition system into a dynamic system by introducing machine learning models that can adapt and evolve. The trained concepts are not fixed but can be updated and refined based on feedback, allowing the system to change its behavior and improve performance over time while maintaining architectural stability.
2Measurement precision
If sophisticated pattern recognition systems are deployed, then the system can recognize known patterns, but it fails to meet user expectations for specific retrieval criteria
Solution Approach 1:
The system segments the retrieval task into distinct components: fixed pattern recognition for known patterns and machine learning-based concept learning for adaptive retrieval. This segmentation allows the system to maintain precision for established patterns while gaining flexibility to learn and adapt to new user-specific criteria through separate learning modules.
3Adaptability or versatility
If the system uses fixed retrieval criteria, then the system operation remains simple, but the system cannot adapt to specific user needs and expectations
Solution Approach 1:
The system implements self-service through automated machine learning processes that continuously improve retrieval accuracy without requiring manual reconfiguration. The system automatically learns from user feedback, adapts its trained concepts, and optimizes retrieval criteria autonomously, maintaining ease of operation while significantly improving adaptability to user needs.
Data Source
AI summary
A system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items. The one or more hardware processors may be further configured to obtain an indication conveying a concept to be learned from the one or more digital media items. The one or more hardware processors may be further configured to receive feedback associated with individual ones of the one or more digital media items. The one or more hardware processors may be configured to obtain individual neural network representations for the individual ones of the one or more digital media items. The one or more hardware processors may be configured to determine a trained concept based on the feedback and the neural network representations of the one or more digital media items.