Automated Network Management Plan Generation Using Aerial Image Analysis

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Solution Overview

Problem

Manual identification and selection of communication equipment for various locations are resource-intensive and impractical due to the large number of potential deployment sites, as different types of equipment provide better performance based on specific characteristics such as data transfer speeds, frequency propagation, and building types.

Innovation Solution

An automated network management plan generation system using computer vision modeling and machine learning processes to evaluate geo-spatial datasets and image-based information, assigning classifications and recommending optimal communication equipment for each location based on its characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual identification and selection of communication equipment is performed for each location, then equipment selection accuracy is improved, but resource consumption and time cost increase significantly

Engineering Contradiction:

Improveequipment selection accuracyVSAvoidtime cost

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses aerial images as copies or representations of physical locations to extract building characteristics without physically visiting each site. Image processing algorithms analyze these visual copies to determine building types, heights, and other relevant features, enabling automated equipment selection while eliminating time-consuming manual site surveys.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces manual mechanical processes (physically visiting locations, manually measuring buildings, hand-selecting equipment) with automated computational processes. Machine learning models and image processing algorithms automatically analyze location data and recommend equipment, substituting human effort with computational intelligence to reduce time cost while maintaining accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

Engineering Contradiction:

Improveequipment selection accuracyVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The system uses aerial images and digital data copies of locations instead of physical site visits. This allows automated analysis of building characteristics through image processing, eliminating the need for manual surveys and reducing resource consumption associated with travel, physical measurements, and on-site assessments.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces resource-intensive manual processes with computational algorithms. Machine learning models automatically process location data and generate equipment recommendations, substituting human labor and physical resources with efficient computational processes that consume minimal energy while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated network management plan generation is implemented, then productivity is improved, but system complexity increases

Engineering Contradiction:

Improvedeployment efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent employs a multi-functional automated system that performs multiple tasks: extracting building characteristics from aerial images, classifying location types, analyzing coverage requirements, and recommending equipment. This universal system handles the entire network management planning process through integrated algorithms, improving productivity while managing complexity through consolidation of functions.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The automated system operates autonomously to generate network management plans without requiring manual intervention at each step. The machine learning models self-adjust and optimize equipment recommendations based on analyzed data, enabling the system to serve itself in generating deployment strategies while maintaining high productivity.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11803576B1Network management plan generation and implementation

Publication Date: 2023.10.31 VERIZON PATENT & LICENSING INC

AI summary

One or more computing devices, systems, and/or methods for constructing and implementing a network management plan are provided. Baseline classifications are assigned to a set of locations. Sub-locations at the set of locations are evaluated to determine whether to override any of the baseline classifications. A model is used to evaluate images depicting the set of locations to generate predicted classifications for the set of locations. Classifications are assigned to the set of locations by implementing conflict resolution rules to selectively retain or replace baseline classifications with predicted classifications. The classifications are used to construct and/or implement a network management plan.