Local Visual Language Model for Private Network Telemetry
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Solution Overview
Problem
Interpreting telemetry data dashboards in private 4G or 5G communications networks is challenging due to complexity and latency issues when using cloud-based visual language models, which can also produce hallucinations leading to incorrect network management.
Innovation Solution
A compact visual language model is deployed locally at the customer site, which processes telemetry data dashboards in near real-time, and is adapted using few-shot learning with labeled training examples. This local model is checked against an independent statistical model to mitigate hallucinations and ensure accurate network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a cloud-based visual language model is used to interpret telemetry data dashboards, then the model can provide comprehensive analysis capabilities, but latency increases and hallucinations occur
Solution Approach 1:
The system segments the visual language model into a compact local version deployed at the edge node and a comprehensive cloud-based version. The local model handles time-critical dashboard interpretation tasks, while the cloud model provides comprehensive analysis capabilities. This segmentation resolves the contradiction by enabling low-latency local processing while maintaining access to comprehensive analysis capabilities through the cloud model when needed.
Solution Approach 2:
The patent introduces an intermediary verification mechanism that checks the output of the local visual language model against expected patterns or additional validation models. This intermediary layer filters out hallucinations and ensures accuracy before acting on the interpretation results, thereby maintaining high measurement precision while benefiting from the low latency of local model execution.
2Adaptability or versatility
If a cloud-based visual language model is used to interpret telemetry data dashboards, then comprehensive analysis is available, but hallucinations lead to incorrect network management
Solution Approach 1:
The system implements feedback mechanisms where the output of the visual language model is verified against additional validation models or expected patterns before being used to trigger network management actions. This feedback loop ensures that only accurate and reliable interpretations lead to network management decisions, thereby improving reliability while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent applies beforehand cushioning by using validation models that pre-check the output of the visual language model for potential hallucinations before the output is acted upon. This protective layer is established in advance to prevent incorrect network management decisions while preserving the comprehensive analysis capabilities of the visual language model.
3Measurement precision
If manual interpretation of telemetry data dashboards is performed, then accuracy can be maintained, but productivity decreases
Solution Approach 1:
The system enables self-service automation where the local visual language model automatically interprets telemetry data dashboards and triggers appropriate network management actions without requiring manual intervention. This self-service capability maintains high productivity and speed of response while the validation mechanisms ensure accuracy, eliminating the need for manual interpretation.
4Loss of time
If a compact local visual language model is deployed, then latency is reduced, but the model may produce hallucinations
Solution Approach 1:
The patent introduces an intermediary verification mechanism that checks the output of the local visual language model against expected patterns or additional validation models. This intermediary layer filters out hallucinations and ensures accuracy before acting on the interpretation results, thereby maintaining high measurement precision while benefiting from the low latency of local model execution.
Solution Approach 2:
The system implements feedback mechanisms where the output of the local visual language model is verified against additional validation models or expected patterns before being used to trigger network management actions. This feedback loop ensures that only accurate and reliable interpretations lead to network management decisions, thereby improving reliability while maintaining fast local processing speeds.
Data Source
AI summary
A management node local to a customer site of a private communications network stores a model. The model is a compact version of a visual language model remote from the customer site. A first screen shot of a dashboard of telemetry data measured from the private communications network is accessed. A prompt is formulated comprising the first screen shot and information to adapt the model to the private communications network via few shot learning. The prompt is submitted to the model. An output is received from the model comprising textual information about anomalies or trends depicted in the first screen shot. The output is checked against data from a statistical model of the telemetry data, the statistical model being independent of the model. In response to the check being successful, an action is triggered to manage the private communications network according to the output.