Universal Message Brokering With AI-Powered In-Platform Adapters
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Solution Overview
Problem
Existing message brokers require additional hardware and are specific to the messaging formats and protocols of two specific computing platforms, limiting their ability to facilitate universal message brokering across disparate systems.
Innovation Solution
Implementing Universal Message Broker (UMB) adapters within computing platforms that use Artificial Intelligence (AI) and Machine Learning (ML) to identify source and target platforms, convert message formats, and predict traffic volume, eliminating the need for additional infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional message brokers are used to enable communication between disparate computing platforms, then message translation between specific formats and protocols is achieved, but additional hardware infrastructure is required and the system lacks universality across different platforms
Solution Approach 1:
The patent implements a universal message broker system that can translate messages between multiple different computing platforms and protocols using a single software-based architecture. The system employs machine learning models trained on diverse platform data to handle various message formats (XML, JSON, SOAP, REST) and protocols (JMS, MQTT, AMQP) without requiring separate hardware brokers for each platform pair, thereby achieving multi-functionality and eliminating the need for additional hardware infrastructure.
Solution Approach 2:
The patent replaces traditional hardware-based message brokers with a software-based system that uses machine learning models for message translation. Instead of relying on physical infrastructure disposed between computing platforms, the invention uses AI algorithms that can be deployed as virtualized services or containerized applications, substituting mechanical/hardware systems with intelligent software systems that provide the same message brokering functionality.
2Reliability
If traditional message brokers with pre-configured logic are deployed, then specific message format translation is ensured, but the system cannot adapt to other computing platforms or message formats
Solution Approach 1:
The patent implements dynamic message translation capabilities using machine learning models that can adapt to different computing platforms and message formats in real-time. The system uses trained ML models that dynamically select and apply appropriate translation logic based on the source and target platforms, rather than relying on static pre-configured rules. This dynamic approach allows the system to maintain high translation accuracy while simultaneously supporting multiple platforms and formats.
Solution Approach 2:
The patent changes the fundamental parameter of message translation from static rule-based logic to dynamic machine learning-based translation. The system uses ML models that have been trained on platform-specific message formats and protocols, allowing the translation parameters to adapt based on the input message characteristics. This enables the system to maintain reliability for specific platform translations while gaining versatility across multiple platforms through the same adaptive mechanism.
3Device complexity
If adapters are deployed within each computing platform to enable universal message brokering, then additional infrastructure is eliminated, but the adapters must accurately identify and translate between multiple message formats and protocols
Solution Approach 1:
The patent implements adapters that act as intermediary components deployed within each computing platform. These adapters serve as local message brokers that translate messages from the platform's native format to universal formats or to formats suitable for the target platform. The adapters use machine learning models to identify the message format and protocol, then apply appropriate translation logic, eliminating the need for centralized infrastructure while maintaining accurate format detection and translation.
Solution Approach 2:
The patent enables computing platforms to perform their own message translation operations through self-contained adapters deployed within each platform. Rather than relying on external message brokers, each platform's adapter autonomously identifies message formats, selects appropriate translation rules, and performs translation locally. This self-service approach eliminates the need for additional centralized infrastructure while distributing the intelligence and capability across the system.
Data Source
AI summary
Universal message brokering is provided across known and future known computing platforms/systems. Message brokering occurs within adapters that are deployed directly within the computing platforms/systems. The adapters rely on Artificial Intelligence (AI) including Machine Learning (ML) to (i) identify the source and the target computing platforms/systems and (ii) convert/translate the messages from the source message format of the source computing platform/system to an identified universal message format and, upon receipt by the target computing platform/system, from the universal message format to the target message format. Further, the UMB adapters may additionally implement AI including ML to predict the volume of further message traffic and, in response, adjust message queues and/or UMB adapter activation in servers of distributed computing platforms/servers.