Context System for Digital Content Object Relationship Clustering
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Solution Overview
Problem
Conventional digital content navigation and browsing systems are limited in determining relationships between objects depicted in digital images, requiring extensive manual curation to identify how objects are related across large digital content collections.
Innovation Solution
A context system generates occurrence contexts by forming a relationship graph using representation learning models to cluster objects into contextual clusters, representing sets of similar relationships between objects, and displays these contexts in a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional search and recommendation systems are used to navigate digital content collections, then image searches and recommendations can be performed, but the systems cannot indicate how objects depicted in digital images are related to other objects
Solution Approach 1:
The patent introduces an intermediary representation learning model that generates relationship embeddings as a mediator between image data and object relationship information. This model processes visual data and transforms it into structured relationship representations that can be queried and displayed, enabling the system to reveal object relationships without fundamentally redesigning the entire search and recommendation architecture.
2Measurement precision
If manual curation is used to identify relationships between objects in digital content collections, then relationship information can be accurately identified, but extensive manual effort and time are required
Solution Approach 1:
The system implements self-service by enabling the representation learning model to automatically generate relationship embeddings and identify object relationships without human intervention. The model processes digital images independently, extracts relationships between objects, and structures this information for display, thereby eliminating the need for manual curation while maintaining accurate relationship identification through learned patterns from the data.
3Quantity of substance
If relationship graphs are generated for all objects in a digital content collection, then comprehensive object relationship information is available, but the complexity of processing and storing this information increases significantly
Solution Approach 1:
The patent applies segmentation by organizing the comprehensive relationship data into structured relationship graphs that are divided into manageable components. Each relationship graph is constructed from discrete relationship embeddings and can be processed, stored, and queried independently. This segmentation allows the system to handle large volumes of relationship data without creating an unmanageable monolithic structure, enabling efficient processing while maintaining comprehensive coverage.
Data Source
AI summary
In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.