Machine Learning Infrastructure Decommissioning

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Solution Overview

Problem

Current methods for identifying infrastructure to decommission in communication networks are manual, labor-intensive, time-consuming, and prone to human error, often relying on geographical information systems, which are inadequate for accurately determining which infrastructure to retire due to incomplete or stale data.

Innovation Solution

A system that processes inputs such as maps, satellite images, and social media images to geocode geographical locations, classify them, and identify infrastructure for potential decommissioning based on cost analysis, using machine learning algorithms to generate a list of recommended locations for decommissioning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If manual visual inspection using GIS is used to identify infrastructure for decommissioning, then the process can be performed with existing tools, but it is manual, laborious, time-consuming, and expensive

Engineering Contradiction:

Improveease of infrastructure identification processVSAvoidproductivity of infrastructure identification

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The patent replaces manual visual inspection processes with automated machine learning-based image processing. The system automatically processes satellite and aerial images to detect and classify infrastructure, eliminating the need for human operators to manually inspect areas while significantly improving processing speed and productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service through automated detection where the machine learning model independently identifies, classifies, and prioritizes infrastructure for decommissioning without requiring human intervention in the analysis process, allowing the system to serve itself in the infrastructure identification task

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If manual visual inspection using GIS is used to identify infrastructure for decommissioning, then existing tools can be utilized, but it is susceptible to human error and inadequate for accurate identification

Engineering Contradiction:

Improveease of infrastructure identification processVSAvoidaccuracy of infrastructure identification

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces manual visual inspection with automated machine learning-based image processing. The system automatically processes satellite and aerial images to detect and classify infrastructure, eliminating the need for human operators to manually inspect areas while significantly improving processing speed and productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the machine learning model continuously improves its accuracy through training on annotated data. The feedback loop involves comparing automated detections with ground truth data to refine the model's performance, ensuring high reliability in infrastructure identification

Inventive Principle:

Principle #23Feedback

3Device complexity

If conventional techniques are used to identify infrastructure, then the process is simple to implement, but it is manual and laborious making it time-consuming and expensive

Engineering Contradiction:

Improvecomplexity of infrastructure identification systemVSAvoidtime required for infrastructure identification

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary actions by pre-processing images and pre-training machine learning models before actual infrastructure identification tasks. This preparation work enables rapid automated processing during execution, reducing the time required for infrastructure identification while maintaining high accuracy

Inventive Principle:

Principle #10Preliminary action

4Measurement precision

If data from multiple sources is processed to improve accuracy, then identification accuracy improves, but the system complexity increases

Engineering Contradiction:

Improveaccuracy of infrastructure identificationVSAvoidcomplexity of data processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges multiple data sources including satellite images, aerial images, and other geospatial data into a unified processing framework. The machine learning model integrates information from these diverse sources to improve infrastructure identification accuracy while managing system complexity through standardized processing pipelines

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS11935097B2Apparatuses and methods for identifying infrastructure through machine learning

Publication Date: 2024.03.19 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, obtaining a first plurality of inputs that identify a plurality of geographical locations and a plurality of infrastructure located at the plurality of geographical locations, classifying each of the plurality of geographical locations in accordance with the first plurality of inputs to obtain a plurality of classes, obtaining a second plurality of inputs that identify costs, revenue, profits, or any combination thereof, associated with the plurality of infrastructure, processing the second plurality of inputs in conjunction with the plurality of classes to identify a first plurality of locations included in the plurality of geographical locations to decommission infrastructure included in the plurality of infrastructure, and presenting the first plurality of locations via a device. Other embodiments are disclosed.