Deep Learning Agent Context Broker for Network Personalization
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Solution Overview
Problem
Enterprise networks face challenges in providing personalized services to clients due to limited contextual information, as interactions are typically individual-based and transient, lacking sharing of context between services.
Innovation Solution
A deep learning agent is employed to monitor traffic flows, extract features, generate a context model using reinforcement learning, and personalize data sent to clients based on determined context, acting as an intermediary to gather and share contextual information across distributed services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual-based service interactions are used, then service simplicity is maintained, but contextual awareness and personalization capability deteriorate
Solution Approach 1:
The patent introduces a context broker as an intermediary component that collects, stores, and manages contextual information from multiple services. This broker acts as a mediator between individual services and clients, enabling personalized services without requiring complex integration between services. The context broker consolidates contextual data and provides it to services that need it, resolving the contradiction by adding personalization capability while maintaining service independence through the intermediary layer.
2Loss of information
If contextual information is not shared between services, then service independence is maintained, but contextual awareness deteriorates
Solution Approach 1:
The patent merges the contextual information management function into a centralized context broker that consolidates contextual data from multiple services. Instead of each service maintaining separate contextual information, the broker combines all contextual information into a unified repository. This merging approach enables comprehensive contextual awareness across services while managing complexity through a single centralized component rather than distributed complexity.
3Measurement precision
If deep learning agents are deployed in each service, then local contextual processing is improved, but overall system complexity and resource consumption worsen
Solution Approach 1:
The patent implements a universal context broker that serves multiple services with a single contextual information management system. Instead of deploying separate deep learning agents in each service, the broker provides centralized contextual analysis capabilities that can be utilized by any service that needs contextual information. This multi-functional approach maintains high contextual analysis accuracy while reducing overall system complexity by eliminating redundant agents across services.
Data Source
AI summary
In one embodiment, a device in a network monitors a plurality of traffic flows in the network. The device extracts a plurality of features from the monitored plurality of traffic flows. The device generates a context model by using deep learning and reinforcement learning on the plurality of features extracted from the monitored traffic flows. The device applies the context model to a particular traffic flow associated with a client, to determine a context for the particular traffic flow. The device personalizes data sent to the client from a remote source based on the determined context.