Lead Evaluation Module for Transaction Request Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Merchant devices face challenges in filtering out leads with incomplete or incorrect information from those with complete and accurate information, leading to inefficiencies in progressing transactions, as existing methods rely on manual and laborious processes.
Innovation Solution
A system utilizing microservices and a lead evaluation module, powered by machine learning, to verify and rank transaction requests based on validated user information, distinguishing between high-potential and low-potential leads, thereby automating the filtering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual filtering of leads is performed, then transaction requests with complete and accurate information can be identified, but significant time and labor resources are consumed
Solution Approach 1:
The patent replaces the manual mechanical filtering process with an automated machine learning system. The lead evaluation module uses trained machine learning models to automatically assess lead quality by analyzing transaction request data, substituting human manual review with computational analysis that operates faster and more consistently.
Solution Approach 2:
The system enables self-service by allowing the lead evaluation module to automatically process and rank leads without requiring manual intervention. The machine learning models independently evaluate transaction requests, assign quality scores, and prioritize leads based on predicted conversion probability, making the filtering process autonomous.
2Reliability
If all transaction requests are reviewed manually, then no leads are missed, but productivity and resource allocation become inefficient
Solution Approach 1:
The system creates a virtual copy of the manual review process through machine learning models that replicate human evaluation capabilities. The lead evaluation module processes transaction requests through multiple analysis layers that mirror comprehensive human review while operating at machine speed, maintaining reliability without sacrificing productivity.
Solution Approach 2:
The patent applies partial action by focusing computational resources on evaluating only the most critical aspects of each lead that predict conversion success. Rather than reviewing every detail of every transaction request, the system identifies and analyzes key predictive features, achieving high reliability through targeted evaluation of the most important lead characteristics.
3Productivity
If automated lead evaluation is implemented, then processing speed and resource allocation improve, but system complexity increases
Solution Approach 1:
The patent segments the lead evaluation system into distinct microservices and modular components. The lead evaluation module is divided into separate machine learning models that handle different aspects of lead assessment independently. This segmentation allows each component to be developed, maintained, and scaled separately, managing complexity while maintaining high processing speed.
Solution Approach 2:
The system introduces an intermediary lead evaluation module that sits between transaction request intake and merchant device notification. This intermediary layer handles the complex machine learning processing, acting as a mediator that translates raw transaction data into prioritized lead rankings, shielding the rest of the system from complexity while enabling automated high-speed processing.
Data Source
AI summary
A computing device (e.g., a server, a cloud-based device, a request management device, etc.), for example, such as a computing device associated with a vehicle dealership and/or the like, may receive a plurality of transaction requests, such as requests to purchase, lease, and/or finance a vehicle. Each transaction request may include user information associated with the transaction, such as a phone number, a home/mailing address, email address, a credit score, vehicle trade-in information, and/or any other data elements. For each transaction request, the computing device may invoke one or more microservices to verify data elements of user information and use responses from the microservices to rank each transaction requests on its potential to progress to a completed transaction.