Multimedia Semantic Annotation Layer for Enterprise Integration
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Solution Overview
Problem
Current enterprise application integration (EAI) systems face challenges in processing unstructured multimedia data, particularly in representing and processing multimodal messages, handling the variety and volume of multimedia data, and addressing user interaction and interoperability issues, which are not adequately covered by existing EIPs and system architectures.
Innovation Solution
The introduction of new architecture components for learning and detecting semantics in multimodal messages, along with methods for modifying and processing multimedia data, such as image resizing, face detection, and converting images to text, to extend current integration foundations with new patterns and operations for multimedia processing, enabling standard user interaction and configuration across industrial and mobile scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current EAI systems process only structured data, then system complexity is low, but they cannot handle unstructured multimedia data with semantic qualities
Solution Approach 1:
The patent introduces a semantic annotation layer as an intermediary between multimedia data and EAI processing. This layer translates unstructured multimedia content into structured semantic representations that existing EAI systems can handle, allowing the system to process diverse media types without fundamentally redesigning the core EAI architecture.
Solution Approach 2:
The patent segments the multimedia processing task into distinct components: data ingestion, semantic annotation, and EAI processing. By dividing the complex task of handling unstructured multimedia data into manageable segments, the system can leverage existing EAI capabilities while adding specialized processing layers where needed.
2Adaptability or versatility
If EAI systems integrate multiple communication partners and protocols, then interoperability improves, but system complexity increases
Solution Approach 1:
The patent implements a universal semantic annotation framework that can handle multiple communication protocols and message formats through a single standardized interface. This multi-functional approach allows the system to integrate diverse communication partners without requiring separate processing paths for each protocol.
Solution Approach 2:
The semantic annotation layer serves as a mediator between diverse communication protocols and the underlying EAI system. It translates various message formats into a unified semantic representation, enabling interoperability while shielding the core system from protocol-specific complexity.
3Ease of operation
If multimedia data is processed in real-time, then user interaction quality improves, but processing time and resources increase
Solution Approach 1:
The patent applies preliminary semantic annotation to multimedia data during ingestion, preparing the data in advance for rapid EAI processing. By performing semantic analysis early in the pipeline, the system reduces the processing time required during real-time user interactions.
Solution Approach 2:
The system implements periodic semantic annotation for high-volume multimedia streams, processing data in batches at optimized intervals. This periodic approach balances real-time responsiveness with efficient resource utilization, maintaining user interaction quality while managing processing loads.
Data Source
AI summary
Systems and methods to modify images, extract features, convert image to text and vice versa, etc., includes deriving multimedia operations from requirements (e.g., resize, crop as modification, mark artifacts in image, face detection for query), and extending current integration foundations (i.e., integration patterns) by new patterns and uses for multimedia along the identified requirements for read, write, update, query operations. Conditions and expressions are defined for pattern configuration and execution as semantic, application-level constructs (e.g., detect face on image, extract address or account data). Patterns to a language are composed with embedded multimedia operations and configuration constructs.