Hybrid Rule-Based and Machine Learning Prediction System
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Solution Overview
Problem
Existing machine learning models face accuracy and efficiency issues due to differences between training data and input data, leading to static predictions and lack of adaptability to new features, requiring costly retraining and lacking interpretability.
Innovation Solution
Integrating rule-based prediction scores and outputs with machine learning models to enable post-training modifications and enhance generalization capabilities, allowing users to create and modify prediction rules with confidence weights, thereby incorporating dynamic knowledge and improving model interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on existing data with static structure, then the model can generate predictions, but the model becomes static and fails to accurately predict when presented with data having different features or structure
Solution Approach 1:
The patent applies dynamics by transitioning from static machine learning models to a dynamic hybrid system that combines rule-based predictions with machine learning models. The system dynamically adjusts predictions based on the structure and features of input data, allowing the model to adapt to new data structures without requiring complete retraining. This resolves the contradiction by enabling the system to maintain accuracy on training data while adapting to new data structures through dynamic rule application.
Solution Approach 2:
The patent merges rule-based prediction systems with machine learning models to create a hybrid approach. The rule-based component handles data with known structures and provides interpretable predictions, while the machine learning component handles complex patterns in training data. This combination allows the system to maintain high accuracy on training data while adapting to new data structures through rule-based reasoning, resolving the contradiction between reliability and adaptability.
2Reliability
If machine learning models are retrained to adapt to new data, then prediction accuracy improves, but retraining is costly and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-defining a set of prediction rules that can be applied to new data structures before retraining is needed. When new data with different structures arrives, the system first attempts to apply existing rules to provide immediate predictions. Only when rules are insufficient does the system initiate retraining, significantly reducing the time loss by handling common cases through pre-prepared rules.
Solution Approach 2:
The patent uses lightweight rule-based predictions as a temporary or disposable solution for handling new data structures. These rules can be quickly created and applied without requiring expensive and time-consuming model retraining. The rules serve as a cost-effective interim solution that can be discarded or updated independently of the main machine learning model, reducing overall retraining time and costs.
3Extent of automation
If machine learning models are used for predictions, then automated predictions can be generated, but the models lack interpretability and user-defined rules cannot be incorporated
Solution Approach 1:
The patent introduces rule-based predictions as an intermediary layer between the machine learning model and the final output. This intermediary component provides interpretable predictions that can be easily explained to users, while still leveraging the automated capabilities of machine learning models for complex pattern recognition. The rule-based layer acts as a mediator that translates complex model outputs into human-understandable predictions, resolving the contradiction between automation and interpretability.
Solution Approach 2:
The patent segments the prediction system into distinct components: rule-based prediction module and machine learning model module. Each component handles specific aspects of prediction - rules provide interpretable, user-defined logic while the ML model handles complex patterns. This segmentation allows the system to maintain automated prediction capability while providing clear interpretability through the rule-based component, resolving the contradiction between automation and information loss.
4Quantity of substance
If machine learning models are trained on limited data, then training cost is reduced, but prediction accuracy deteriorates especially for divergent input data
Solution Approach 1:
The patent applies universality by creating a hybrid system where rule-based predictions can handle multiple types of data structures and domains. The rules are designed to be broadly applicable across different scenarios, allowing the system to maintain high prediction accuracy even with limited training data by leveraging universal rule patterns. This multi-functional approach enables the system to generalize well to divergent input data without requiring extensive domain-specific training data, resolving the contradiction between training data quantity and prediction accuracy.
Data Source
AI summary
A computer-implemented method for generating a predictive output based on a predictive input includes generating a plurality of rule-based prediction scores by executing one or more prediction rules on the prediction input, wherein each prediction rule of the one or more prediction rules is associated with a rule condition and one or more predictive weights, each predictive weight of the one or more predictive weights is associated with a related prediction category of a plurality of prediction categories, and each prediction category of the plurality of prediction categories is associated with a rule-based prediction score; determining a rule-based prediction output based at least in part on the plurality of rule-based prediction scores; and providing the plurality of rule-based prediction scores and the rule-based prediction output to a machine learning engine, wherein the machine learning engine is configured to generate a machine-learning based prediction output based at least in part on the plurality of rule-based prediction scores and the rule-based prediction output.