Contextual AI Inference Gateway for Dynamic Model Selection
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Solution Overview
Problem
Current AI inference services in IoT networks do not effectively consider client contextual data, changing device characteristics, and the need for dynamic selection of appropriate AI models, leading to inefficient resource usage and suboptimal performance.
Innovation Solution
The implementation of a distributed and contextualized AI inference service that uses a gateway to redirect requests to appropriate AI inference appliances based on sensor data and client device capabilities, allowing for dynamic selection and execution of AI model variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple AI service variants are exposed through interfaces at the edge-accessible cloud, then clients can access different service types, but significant configuration and pre-processing must be utilized to employ AI inference models
Solution Approach 1:
The patent introduces an AI service gateway as an intermediary component that sits between clients and multiple AI service variants. The gateway automatically discovers available services, matches client requests to appropriate service instances based on contextual data, and handles the complexity of service selection and configuration. This mediator approach resolves the contradiction by providing versatile service access while shielding clients from configuration complexity.
Solution Approach 2:
The system implements self-service mechanisms where the AI service gateway autonomously performs service discovery, client capability assessment, and optimal service instance selection without requiring manual configuration. The gateway dynamically adapts to changing device characteristics and sensor data availability, automatically routing requests to the most appropriate AI service variant. This self-service approach enables versatile service access while eliminating the need for significant pre-processing and configuration.
2Ease of operation
If clients include programming and rules that explicitly request a particular service variant, then specific AI inference models can be invoked, but significant configuration must be utilized
Solution Approach 1:
The patent implements preliminary action by having the AI service gateway pre-discover and catalog available AI service variants along with their requirements and capabilities before any client requests arrive. The gateway maintains an up-to-date registry of services, their supported input types, and optimal usage conditions. When clients make requests, the gateway instantly matches them to pre-identified appropriate services, eliminating the need for clients to perform time-consuming configuration or programming of service selection rules.
Solution Approach 2:
The gateway acts as an intermediary that absorbs the configuration burden on behalf of clients. It maintains service registry information and automatically translates simple client requests into appropriately configured service invocations. This mediator approach simplifies client operations while avoiding configuration time losses by pre-processing service discovery and matching logic.
3Productivity
If AI inference services do not consider client contextual data and device characteristics, then service deployment is simplified, but resource usage becomes inefficient and performance is suboptimal
Solution Approach 1:
The patent applies local quality by having the AI service gateway assess specific client device characteristics, available sensors, and contextual data to dynamically select the most appropriate AI service variant for each request. Different clients receive different service instances optimized for their specific capabilities and needs. This localized adaptation approach maximizes inference performance by matching service capabilities to client characteristics while managing architecture complexity through automated gateway-based decision logic.
Solution Approach 2:
The system implements dynamics by enabling the gateway to adapt service selections in real-time based on changing device characteristics, sensor data availability, and environmental conditions. The gateway continuously monitors client context and dynamically routes requests to the most suitable AI service variant, allowing the system to optimize performance for each specific situation. This dynamic adaptation improves productivity while the gateway manages the complexity of handling multiple service variants and their different requirements.
4Reliability
If the system dynamically selects AI model variations based on sensor data and device capabilities, then optimal inference results are achieved, but resource selection and routing complexity increases
Solution Approach 1:
The AI service gateway serves as an intermediary that centralizes the complexity of dynamic service selection and routing logic. It implements the decision-making framework that evaluates client device capabilities, available sensor data, and contextual information to select the most appropriate AI model variation. By concentrating this complex routing logic in the gateway rather than distributing it across multiple components, the system achieves high inference reliability while managing routing complexity in a centralized, maintainable manner.
Solution Approach 2:
The patent segments the system into distinct functional components: the AI service gateway that handles discovery and selection logic, the registry that stores service variant information, and the client devices that generate requests. This segmentation allows the complex routing and selection logic to be isolated in the gateway component, making it easier to manage and maintain. The segmentation enables reliable dynamic selection based on multiple factors while containing routing complexity within a dedicated component rather than spreading it throughout the entire system.
Data Source
AI summary
Various systems and methods of initiating and performing contextualized AI inferencing, are described herein. In an example, operations performed with a gateway computing device to invoke an inferencing model include receiving and processing a request for an inferencing operation, selecting an implementation of the inferencing model on a remote service based on a model specification and contextual data from the edge device, and executing the selected implementation of the inferencing model, such that results from the inferencing model are provided back to the edge device. Also in an example, operations performed with an edge computing device to request an inferencing model include collecting contextual data, generating an inferencing request, transmitting the inference request to a gateway device, and receiving and processing the results of execution. Further techniques for implementing a registration of the inference model, and invoking particular variants of an inference model, are also described.