Intelligent Media Routing via Feature Extraction
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Solution Overview
Problem
Existing systems face challenges in efficiently organizing and sharing digital media items, such as photos, videos, and text files, due to difficulties in metadata-based searches and the complexity of managing edited, deleted, or added media within collections.
Innovation Solution
A system comprising hardware processors that execute computer program components for feature extraction, grouping, and routing of media items, which analyzes metadata and other information to automatically route media items to relevant users or groups, and suggests sharing based on user interests and metadata analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metadata-based search is used to organize and retrieve media items, then users can locate media items through keyword searches, but users still have to sift through multiple results to find the most relevant ones, reducing efficiency
Solution Approach 1:
The system segments the large collection of media items into multiple organized collections based on extracted features and metadata. Instead of presenting all matching items in a flat list, the system divides results into meaningful groups (e.g., by event, location, person, or time period), allowing users to quickly navigate to relevant segments without sifting through unrelated items.
Solution Approach 2:
The system performs preliminary organization and tagging of media items by automatically extracting features (such as facial recognition, object detection, scene analysis) and assigning them to appropriate collections before the user initiates a search. This pre-organization ensures that when a user searches, the results are already filtered and structured, eliminating the need for manual sifting.
2Ease of operation
If manual organization of media items into collections is implemented, then users can categorize media items, but the process becomes complex when media items are edited, deleted, or added to existing collections
Solution Approach 1:
The system automatically performs organization tasks by extracting features from media items (such as identifying people, places, events, and objects) and autonomously assigning items to appropriate collections. This self-service capability eliminates the need for users to manually organize media, and the system automatically handles additions, deletions, and edits without requiring complex user intervention or system reconfiguration.
Solution Approach 2:
The system uses dynamic parameter extraction from media items (such as metadata, visual features, audio characteristics) to automatically determine collection assignments. When media items are added, edited, or deleted, the system re-evaluates their parameters and automatically adjusts their organizational placement, maintaining up-to-date collections without manual intervention.
3Productivity
If automatic feature extraction and routing system is implemented, then media items are automatically routed to relevant users based on analyzed information, but the system requires complex processing of metadata and media content
Solution Approach 1:
The system employs multi-functional processing components that can extract various types of features (visual, audio, metadata) using the same underlying infrastructure. The feature extraction system, grouping logic, and routing mechanism work together as an integrated multi-functional platform that handles different media types and organization scenarios, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system introduces an intermediary layer of intelligent grouping that sits between raw media items and final user routing. This intermediary component analyzes extracted features, determines appropriate collections, and generates routing decisions, effectively mediating the complex processing tasks and distributing the computational burden across multiple specialized sub-functions.
Data Source
AI summary
In certain embodiments, automated routing of media items between user devices may be facilitated. In some embodiments, a routing computer system may automatically obtain images or videos from one or more sources. The routing computer system may perform object recognition on the contents of the images or videos to identify individuals or other objects in the images or videos. The routing computer system may assign first and second images or videos of the images or videos to a first media item group based on (i) the first and second images or videos having similar metadata and (ii) the object recognition identifying an individual or object in the first image or video and an individual or object in the second image or video that are similar to each other. The routing computer system may automatically transmit the first image or video to one or more user devices based on the assignment.