Edge Model for Natural Language Statistical Insights
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Solution Overview
Problem
Existing 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 involving artificial intelligence and machine learning.
Innovation Solution
The implementation of an edge model that processes multiple disparate streams of time-series data, determines statistical relationships, and provides natural language interactions, enabling insights and foresights with statistical support. This edge model operates at edge locations that may be disconnected from conventional or cloud-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If centralized processing is used, then data can be processed through a single location, but latency increases and real-time processing capability deteriorates
Solution Approach 1:
The patent divides the centralized processing function into distributed edge computing nodes deployed at multiple locations. Each edge node independently processes data locally, eliminating the single centralized processing bottleneck and reducing latency by enabling parallel processing across distributed segments.
Solution Approach 2:
The patent transitions from a single-point (centralized) processing architecture to a multi-point (distributed) processing architecture by adding the spatial dimension of deployment locations. This dimensional change enables simultaneous processing at multiple locations, improving overall processing speed and reducing latency.
2Ease of operation
If centralized processing is used, then system management is simplified, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts data processing functions from the centralized system and places them at edge nodes. By taking out processing capabilities from the central location and distributing them to edge nodes, the system reduces the volume of data that must traverse the network, thereby decreasing bandwidth consumption while maintaining manageable system operations.
Solution Approach 2:
The patent performs data processing actions preliminarily at edge nodes before data reaches the centralized system. This preliminary processing filters, aggregates, or transforms data locally, reducing the quantity of data that needs to be transmitted over the network, thus decreasing bandwidth requirements.
3Productivity
If centralized processing is used, then processing capacity can be aggregated, but data privacy and network security risks increase
Solution Approach 1:
The patent segments data processing into isolated edge node instances that operate independently. Each edge node processes data locally and only communicates necessary aggregated information with the centralized system, reducing the attack surface and minimizing privacy and security risks associated with centralized data storage and processing.
Solution Approach 2:
The patent introduces edge nodes as intermediary components between data sources and the centralized system. These intermediaries process and filter data locally, acting as security layers that prevent direct exposure of sensitive data to centralized systems, thereby reducing network security and data privacy risks.
4Loss of time
If edge processing is implemented, then latency is reduced and real-time capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a standardized edge node platform that can perform multiple processing functions (data filtering, aggregation, analysis, and communication) through unified software frameworks. This multi-functionality approach reduces the complexity of deploying and managing multiple specialized devices, while still achieving low-latency processing through distributed edge nodes.
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.