Context-Aware Media Curation via Cognitive Inference
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Solution Overview
Problem
Current information retrieval systems for media, such as images and videos, rely heavily on manual metadata and basic analysis techniques like face recognition or color analysis, failing to consider higher-level contextual information, making it difficult for users to effectively access and retrieve media in a context-aware manner.
Innovation Solution
A system that utilizes a computing device to capture and analyze context data from various sources, including location, user behavior, social activity, and audio, to infer higher-order context and associate it with media objects, enabling context-aware media curation and experiences by leveraging cognitive and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual metadata and basic analysis techniques (face recognition, color analysis) are used for media retrieval, then the system is simple to implement, but it fails to consider higher-level contextual information and cannot provide context-aware media curation
Solution Approach 1:
The system segments the context analysis into multiple independent modules: location data capture, user behavior data capture, social activity data capture, and audio data capture. Each module processes specific type of context information separately before integrating them through cognitive algorithms, making the complex system manageable and maintainable while achieving comprehensive context-aware media curation
Solution Approach 2:
The patent introduces cognitive algorithms as an intermediary layer between raw context data from various sources and the final media retrieval/results. This intermediary processes and synthesizes multiple data types (location, behavior, social, audio) into higher-order context representations, enabling context-aware curation without directly implementing all complex analysis functions
2Measurement precision
If multiple types of context data (location, user behavior, social activity, audio) are captured and analyzed, then higher-order context inference is achieved, but the system complexity and processing requirements increase
Solution Approach 1:
The system merges multiple types of context data (location, user behavior, social activity, audio) into a unified context representation through cognitive algorithms. By combining these diverse data sources, the system achieves more accurate higher-order context inference about media content and user intentions, while the merging process itself is managed through standardized integration protocols
3Adaptability or versatility
If cognitive algorithms are used to infer higher-order context, then media curation becomes more intelligent and personalized, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary processing of context data by capturing and organizing raw data from various sources before applying cognitive algorithms. Location data is pre-processed to identify patterns, user behavior data is pre-organized to track sequences, and social activity data is pre-filtered for relevance. This preliminary action reduces the computational burden on cognitive algorithms during the actual inference process, enabling personalization with lower resource consumption
Data Source
AI summary
Technologies for automated context-aware media curation include a computing device that captures context data associated with media objects. The context data may include location data, proximity data, behavior data of the user, and social activity data. The computing device generates inferred context data using one or more cognitive or machine learning algorithms. The inferred context data may include semantic time or location data, activity data, or sentiment data. The computing device updates a user context model and an expanded media object graph based on the context data and the inferred context data. The computing device selects one or more target media objects using the user context model and the expanded media object graph. The computing device may present context-aware media experiences to the user with the target media objects. Context-aware media experiences may include contextual semantic search and contextual media browsing. Other embodiments are described and claimed.