ML-Based Fee Generation for Telecom Projects
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Solution Overview
Problem
In the telecommunications industry, determining project fees is a manual and time-intensive process that is often error-prone and lacks accurate comparison across similar projects, leading to uncertainty and variant pricing.
Innovation Solution
A machine learning-based system that trains on historical data to identify similar projects and generate accurate base fee prices by correlating project attributes with final fee prices, using a trained model to automate the fee generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual fee pricing negotiation is used, then flexibility in fee determination is maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical project data before actual fee pricing is needed. The model learns correlations between project attributes and final fees in advance, so when a new project comes in, the fee can be quickly estimated without manual negotiation from scratch. This resolves the contradiction by preparing the pricing intelligence beforehand, reducing real-time negotiation time while maintaining accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of fee negotiation with an automated machine learning system. The ML model automatically processes project attributes, compares them against historical data, and generates fee estimates without human intervention. This substitution eliminates the time-consuming manual negotiation process while preserving the flexibility to adapt to different project types through the model's learning capability.
2Loss of information
If manual comparison of project pricing information is performed, then some pricing insights can be obtained, but the process is error-prone and limited to small subsets of projects
Solution Approach 1:
The machine learning model serves multiple functions: it stores historical pricing data, performs attribute-based project similarity matching, identifies correlation patterns between attributes and fees, and generates predictions. This single universal system replaces multiple manual processes (data collection, comparison, analysis) and can handle any number of projects simultaneously, not just small subsets. The model processes all historical projects uniformly, eliminating errors from manual selection and comparison limitations.
Solution Approach 2:
The system creates a digital copy of historical project data and pricing information in structured format, allowing exact replication and comparison without manual intervention. The ML model learns from these copied historical patterns and applies them to new projects. This copying approach ensures consistent, error-free access to complete historical data sets, unlike manual processes that are limited to small subsets and prone to copying errors.
3Adaptability or versatility
If case-by-case negotiated pricing is used, then each project can be individually considered, but fee pricing becomes highly variant even for similar projects
Solution Approach 1:
The system applies local quality by considering specific project attributes (location, technology type, scale) that are locally relevant to each project while maintaining overall consistency. The ML model identifies which attributes are most important for pricing decisions and weights them appropriately. This allows individual project characteristics to be considered without allowing them to create excessive variance - similar projects with similar attribute profiles receive consistent pricing, while genuinely different projects receive differentiated pricing based on their specific attributes.
Solution Approach 2:
The patent transforms the pricing approach by changing parameters from subjective negotiation variables to objective, data-driven parameters. The ML model uses specific measurable parameters (project attributes from historical data) to determine pricing, replacing the highly variable human negotiation process. This parameter change enables systematic consideration of individual project features while constraining the output to consistent, comparable fee structures across all projects. The model learns the optimal parameter relationships from historical data, achieving both adaptability and precision.
Data Source
AI summary
Directed to methods and systems for automated fee generation through the use of machine learning for telecommunications projects. Exemplary implementations may: receive, by a sales support microservice in communication with a trained model running on a server, a plurality of attributes; and feed the plurality of attributes to the trained model. The trained model can retrieve historical data regarding fee prices, identify similar historical projects based on final fee prices and historical attributes of the respective projects, and refine a correlation between historical attributes and fee prices, which can be used in the generation of a fee price to provision a telecommunications project as a result of those attributes.