Machine Learning Model Serialization for Real-Time Lead Matching
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Solution Overview
Problem
Existing lead management systems in service industries, such as financial services, rely on generalized rules and human-biased decisions for matching prospective clients with advisors, leading to sub-optimal outcomes due to data incompatibility and large batch data issues, making it infeasible to effectively use machine learning ecosystems.
Innovation Solution
A computer-implemented method and system that trains a machine learning model using known attributes of leads and advisors to provide optimal matching recommendations, serializes the model for platform-independent access, and executes it on an end-user device with user preferences, filtering recommendations using business rules and protocols to accommodate diverse data sets and provide real-time advisor matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model is trained using large batch data, then matching accuracy is improved, but data compatibility issues and system complexity increase making it infeasible
Solution Approach 1:
The patent segments the machine learning model into a trained component (trained on large batch data for accuracy) and an execution component (deserialized model that runs on end-user devices). This allows the system to benefit from large-scale training without requiring the entire system to handle data compatibility issues, as the deserialized model only requires essential input features to function.
Solution Approach 2:
The patent introduces an intermediary serialization/deserialization process that acts as a bridge between the training phase (which requires large batch data) and the execution phase (which operates on individual queries). The serialized model file serves as an intermediary artifact that encapsulates the learned patterns without requiring the original large dataset to be present during execution.
2Power
If machine learning model is executed on centralized server, then data processing capability is improved, but real-time response speed and user autonomy deteriorate
Solution Approach 1:
The patent extracts the machine learning model from the centralized server environment and places it directly on end-user devices through serialization. This allows the computational power to be distributed, with the model executing locally on user devices for immediate real-time responses, while the server retains responsibility for model training and updates.
3Measurement precision
If machine learning model requires direct connection to lead-management ecosystem, then data accuracy is improved, but system accessibility and ease of operation worsen
Solution Approach 1:
The patent creates a copy of the machine learning model in serialized form that can be distributed and executed independently of the original lead-management ecosystem. This copy contains the essential matching logic and can operate with simplified input data, allowing users to access advisor recommendations without requiring direct connection to or deep integration with the complex lead-management system.
Data Source
AI summary
A computer-implemented method for providing recommendations for matching leads to advisors using a machine learning model. The method comprises training the machine learning model using attributes of leads and advisors to determine optimal matches and output a recommendation list of lead/advisor matched pairs, serializing the machine learning model in a platform independent manner, providing access to the serialized machine learning model to an end user device via an API, deserializing the machine learning model, receiving preferences from the end user, executing the deserialized machine learning model at the end user device using the received preferences to determine a list of advisor recommendations, filtering the list of recommendations using rules which disallow certain lead/advisor pairs, outputting filtered recommendations at the end user device for viewing and transmitting a selection from the filtered recommendations from the end user for further training of the machine learning module with respect to the end user.