Dynamic Models for Multi-Source Trigger Event Action Prediction
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Solution Overview
Problem
Existing systems fail to effectively manage and leverage data from multiple sources to dynamically generate models that predict customer actions based on trigger events, leading to inefficiencies in customer loyalty management.
Innovation Solution
A database system that filters and processes data from multiple sources to identify trigger events, generates dynamic models to predict customer actions, and sends notifications based on these models, using hardware processors and communication circuits to ensure data conformity and model application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sources is collected and processed to generate dynamic models, then prediction accuracy and customer loyalty management effectiveness are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments data from multiple sources into distinct datasets (customer data, vehicle data, market data) and processes them through separate filtering and modeling stages. This modular segmentation reduces overall system complexity while maintaining comprehensive data processing capabilities for accurate predictions.
Solution Approach 2:
The patent introduces intermediary processing layers including data filtering mechanisms and feature selection modules that mediate between raw multi-source data and final predictive models. These intermediaries simplify the data-flow architecture and reduce computational complexity while preserving prediction accuracy.
2Speed
If dynamic models are generated and applied in real-time, then responsiveness to customer actions is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and filtering data in advance, creating ready-to-use feature sets and model parameters that can be quickly applied to new data points. This pre-computation reduces real-time computational requirements while maintaining fast response times for predicting customer actions.
Solution Approach 2:
The patent employs parameter changes by adjusting model complexity and computational parameters based on data characteristics and prediction requirements. The system dynamically modifies processing intensity and model parameters to achieve optimal balance between response speed and computational resource consumption.
Data Source
AI summary
Embodiments of a system may comprise databases and a processor that accesses a first database and verifies that each of the plurality of client records conforms to a set of formatting guidelines and filters the records in the first database into a vehicle data subfile and a client data subfile, monitors the records in the vehicle and client data subfiles for a trigger event, generates one or more dynamic models for determining a likelihood that a client will perform an action based on the trigger event, applies the generated dynamic model(s) to generate at least one score value associated with the record including the trigger event, and generates a notification including one or more of the generated at least one score value, the one or more trigger event records, and information associated with the one or more trigger event records for transmission to a user.