Adjustable Edge Execution Models for Secure Usage Data Processing

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Solution Overview

Problem

Conventional techniques for collecting and processing usage data from remotely managed infrastructure face challenges such as inefficiency, high computational resource requirements, and data security and compliance issues, particularly when dealing with diverse customer subscriptions and geographical variations.

Innovation Solution

Implementing a set of function blocks on customer locations to process usage data, using a machine learning model to automatically select and orchestrate these blocks based on subscription details, reducing the need for additional hardware resources and enhancing data security.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If usage data is collected at customer location and sent to central location for processing, then data processing can be performed, but processing speed is slow and efficiency is reduced

Engineering Contradiction:
Improvedata processing efficiencyVSAvoidprocessing time
Core Design Contradiction:
ProductivityVSLoss of time

Solution Approach 1:

Instead of collecting data at the customer location and sending it to a central location for processing (conventional approach), the patent inverts the processing location to the edge device at the customer premises. The edge device executes function blocks locally to process usage data, eliminating the time-consuming data transmission and central processing bottleneck while maintaining accurate billing and reporting capabilities.

Inventive Principle:
Principle #13The other way round (Inversion)

2Reliability

If conventional data collection techniques are used, then usage data can be processed, but data security and compliance issues arise

Engineering Contradiction:
Improvedata security and complianceVSAvoiddata security risks
Core Design Contradiction:
ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent segments the data processing function into distributed edge devices at customer locations rather than centralized processing. Each edge device processes usage data locally using function blocks, which eliminates the security risks associated with transmitting sensitive usage data across networks. This segmentation maintains data security and compliance while enabling accurate billing and reporting.

Inventive Principle:
Principle #1Segmentation

3Adaptability or versatility

If diverse customer subscriptions and geographical variations are handled, then service coverage is improved, but system complexity increases

Engineering Contradiction:
Improveservice coverageVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal function block architecture where a single edge device can execute different function blocks to handle diverse customer subscriptions and geographical variations. The function blocks are configurable and can be dynamically selected based on subscription type and location requirements, providing service versatility without increasing hardware complexity. This modular approach allows the same edge device infrastructure to serve multiple service types and regions.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Data Source

PatentUS12379912B2Adjustable execution models for processing usage data for remote infrastructure
Publication Date: 2025.08.05 DELL PROD LP
  • US12379912B2 patent drawing
  • US12379912B2 patent drawing
  • US12379912B2 patent drawing

AI summary

Methods, apparatus, and processor-readable storage media for processing usage data for remote infrastructure using adjustable execution models are provided herein. An example computer-implemented method includes: obtaining a data structure related to a subscription request for hardware infrastructure provided by a service provider, wherein the data structure is obtained by a processing engine deployed on the hardware infrastructure at a remote location; analyzing the data structure to build an execution model comprising function blocks for processing usage data associated with the hardware infrastructure and one or more instructions to execute the function blocks; processing the usage data for a given time period in accordance with the execution model; providing execution data associated with the plurality of function blocks; and automatically adjusting the execution model for an additional time period using information obtained from the service provider, wherein the obtained information is based at least in part on the execution data.