LLM Conversational Analytics for Network Telemetry
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Solution Overview
Problem
Existing network management systems rely heavily on human intervention and are inefficient in processing and analyzing large volumes of network telemetry data, making it difficult to proactively identify issues, optimize network traffic, and detect security incidents.
Innovation Solution
The implementation of a Generative AI framework that leverages Large Language Models (LLMs) to process and analyze network telemetry data, providing a conversational interface for quick insights and enabling proactive network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually analyze network telemetry data, then analysis precision is maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual human analysis with an automated LLM-based system that processes network telemetry data. The system converts network state data into summaries and transforms them into training prompts for LLMs, enabling automated insight generation that maintains analytical precision while dramatically reducing processing time and eliminating manual labor requirements.
Solution Approach 2:
The patent creates a virtual copy of human expert analysis capability through the LLM system. By training the model on network telemetry data and converting it into a conversational analytics interface, the system replicates expert analytical functions without requiring actual human experts to perform the analysis manually.
2Productivity
If conventional network management systems are used, then system simplicity is maintained, but productivity in processing network telemetry data is low
Solution Approach 1:
The patent introduces an intermediary layer between raw network telemetry data and user insights. This intermediary consists of the LLM processing pipeline that converts network state data into summaries, transforms them into training prompts, and generates conversational responses. This intermediary layer handles the complexity of data processing while presenting a simple conversational interface to users.
Solution Approach 2:
The patent creates a universal analytics platform where a single LLM-based system can handle multiple network management tasks including issue identification, traffic optimization, capacity planning, and security incident detection. The conversational interface provides multi-functional capabilities through a unified system rather than requiring separate specialized tools.
3Ease of operation
If manual intervention is required for each analysis step, then control and accuracy are maintained, but ease of operation decreases for strategic decision-makers
Solution Approach 1:
The patent implements a self-service analytics system where the LLM automatically performs data processing, pattern recognition, and insight generation without requiring user intervention in technical steps. Users simply provide natural language queries and receive processed insights, eliminating the need for manual data manipulation while maintaining high automation in the analytical pipeline.
Solution Approach 2:
The patent segments the complex analysis process into distinct automated stages: data intake, summary conversion, prompt transformation, and insight generation. Each stage is handled automatically by the LLM system, separating the user interaction layer from the processing layers while maintaining full automation throughout the workflow.
Data Source
AI summary
In some implementations, the method may include receiving, by one or more agent applications, a query from a user. In addition, the method may include providing a dataframe to the one or more agent applications. The method may include appending a predetermined number of initial entries from the dataframe to a suffix of the query. Moreover, the method may include constructing a standardized prompt template, where the query is embedded within the standardized prompt template. Also, the method may include Channeling the prompt template to one or more Large Language Models (LLMs). Further, the method may include Utilizing a GPT API to generate a generated code snippet. In addition, the method may include Executing the generated code snippet to create a resulting output. The method may include Relaying the resulting output back to the user.