Edge Model for Time Series Data Natural Language Insights
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Solution Overview
Problem
Centralized cloud computing architectures face challenges in latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in real-time, especially for applications like AI and ML workloads.
Innovation Solution
The implementation of an edge model that processes multiple disparate streams of data, determines statistical relationships, and provides natural language insights and foresights, capable of operating at edge locations disconnected from cloud systems, using a training dataset generated from existing models and fine-tuned for specific edge location data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted to centralized data centers for processing, then processing capacity is improved, but latency and bandwidth usage worsen
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge computing nodes deployed at multiple locations. Each edge node independently processes data locally, eliminating the need to transmit all data to centralized data centers. This segmentation resolves the contradiction by maintaining processing capacity through distributed computation while reducing latency by processing data closer to its source.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge computing nodes across geographically distributed locations rather than relying on a single centralized data center. This dimensional change enables parallel processing across multiple nodes, improving overall processing capacity while reducing the distance data must travel, thereby reducing latency.
2Productivity
If data is transmitted to centralized data centers for processing, then processing capacity is improved, but bandwidth usage worsens
Solution Approach 1:
By segmenting the processing workload across distributed edge nodes, the patent eliminates the need to transmit large volumes of data over the network to centralized data centers. Each edge node processes data locally, dramatically reducing bandwidth consumption while maintaining collective processing capacity across the distributed system.
Solution Approach 2:
The patent extracts the processing function from centralized data centers and places it at edge locations closer to data sources. This extraction removes the requirement for extensive data transmission over the network, reducing bandwidth usage while preserving processing capacity at the edge nodes.
3Productivity
If centralized processing is used, then data processing capability is improved, but data privacy and network security worsen
Solution Approach 1:
The patent segments data processing across multiple distributed edge nodes rather than consolidating it in centralized data centers. This segmentation improves data privacy and security by ensuring that sensitive data remains localized at edge nodes, reducing exposure during transmission and storage. Each node processes only the data it receives locally, minimizing network exposure while maintaining collective processing capability.
4Reliability
If edge model operates disconnected from cloud systems, then availability is improved, but model adaptability worsens
Solution Approach 1:
The patent applies preliminary action by pre-training the edge model on comprehensive datasets before deployment to edge locations. The model is pre-adapted to handle various data types and processing scenarios, enabling it to operate effectively even when disconnected from cloud systems. This preliminary preparation ensures both availability during disconnection and maintained adaptability through pre-loaded knowledge.
Solution Approach 2:
The edge model is designed to be self-sufficient at edge locations, capable of processing data and providing insights without requiring continuous connection to centralized systems. This self-service capability ensures availability during disconnection while maintaining adaptability through local processing of diverse data types that the model was pre-trained to handle.
Data Source
AI summary
Disclosed implementations include systems, methods, and apparatus that process multiple, disparate streams of data, determine correlations and relationships between the data and provide natural language responses that provide insights for events or activities that have occurred and foresights for events or activities that are forecasted to occur. The disclosed implementations include a model that understands data statistics and provides both insights and foresights that are backed with statistical support that can be presented to and understood by operators. Still further, the disclosed implementations are capable of operating at edge locations that may be frequently or permanently disconnected from conventional or cloud based systems.