Geospatial Cohort Ranking for Communication Network Build Sequencing
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Solution Overview
Problem
Conventional methods for deploying communication networks, such as Fiber-to-the-Home (FTTH) networks, rely on static data and predefined heuristics, lacking adaptability to new data and are capital-intensive, requiring service providers to demonstrate expansion benefits and navigate regulatory hurdles.
Innovation Solution
A system and method that utilizes geospatial data, machine learning, and multi-factor analysis to dynamically select and prioritize geographic areas for network deployment, incorporating buffering, graph algorithms, and cohort modeling to optimize build sequencing and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional static data and predefined heuristics are used for network deployment, then implementation simplicity is maintained, but deployment efficiency and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic cohort modeling that adapts to new data continuously. The system transitions from static predefined heuristics to dynamic machine learning models that retrain and update deployment priorities based on incoming geospatial data, infrastructure changes, and demand patterns, thereby improving deployment efficiency while adapting to changing conditions
Solution Approach 2:
The system incorporates feedback loops where deployment outcomes and new geospatial data are fed back into the machine learning models. The models continuously retrain on updated data, allowing the system to learn from past deployments and improve future decisions, resolving the contradiction between simplicity and efficiency
2Area of stationary object
If capital-intensive expansion methods are used, then network coverage is improved, but return-on-investment and cost-effectiveness deteriorate
Solution Approach 1:
The patent changes the parameter of area selection from uniform or heuristic-based to data-driven prioritization. By using machine learning models to score and rank geographic cohorts based on multiple factors (infrastructure proximity, demand density, competitive landscape), the system optimizes capital allocation to achieve maximum coverage expansion per dollar spent
Solution Approach 2:
The system performs preliminary analysis and prioritization of deployment areas before actual construction begins. By pre-identifying high-value cohorts using geospatial data and machine learning, the system ensures that capital is allocated to the most promising areas first, improving return-on-investment before deployment activities commence
3Speed
If manual area selection processes are used, then process simplicity is maintained, but deployment speed and precision deteriorate
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning systems. The system uses computational algorithms to process geospatial data, perform cohort modeling, and generate deployment priorities automatically, dramatically increasing deployment speed while handling complex multi-factor analysis that would be impractical manually
Solution Approach 2:
The system creates computational models that replicate and automate the decision-making process. By encoding deployment criteria and analysis logic into machine learning algorithms, the system copies expert judgment at scale, enabling rapid evaluation of multiple geographic areas simultaneously without manual intervention
4Measurement precision
If comprehensive multi-factor analysis is performed, then deployment precision is improved, but computational complexity and processing time worsen
Solution Approach 1:
The patent segments the geographic area into discrete cohorts or blocks that can be analyzed independently and in parallel. By dividing the overall deployment problem into smaller spatial units, the system can apply comprehensive multi-factor analysis to each segment efficiently, then aggregate results to determine overall deployment priorities
Solution Approach 2:
The system performs analysis on a subset of prioritized cohorts rather than exhaustively analyzing every possible area. By using machine learning to identify and focus computational resources on the most promising geographic segments, the system achieves high precision in area selection while reducing overall processing time through selective analysis
Data Source
AI summary
The system and method are provided for deployment of communication networks. The method includes obtaining geospatial data corresponding to blocks within a geographical area. The method also includes selecting as the respective cohort a block of a plurality of blocks. The method also includes adding a buffer zone of a predetermined size around a cohort. The method also includes appending to the cohort blocks that touch the cohort or intersect with the buffer zone. The method also includes repeating the adding and appending until there are no further blocks to add to the cohort. The method also includes ranking the cohorts based on the total number of premises within each cohort, and selecting where to deploy one or more communication networks based on the ranking.