Real-Time Variable Prediction Using Regression Trees
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Solution Overview
Problem
In financial planning and insurance simulations, customers often face difficulties in providing accurate input due to psychological hurdles or lack of knowledge, leading to incomplete or inaccurate answers, especially when faced with a large number of parameters.
Innovation Solution
A system that allows users to input a first set of variables while predicting the values of a second set in real-time using regression models and decision trees, which dynamically adjust based on input values and missing data, reducing the need for extensive pre-computation of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers are required to answer a large number of questions in an answer sheet, then the simulation can be performed with sufficient parameters, but customers may give up answering difficult questions or provide inaccurate answers due to psychological hurdles and lack of knowledge
Solution Approach 1:
The system automatically predicts and fills in difficult-to-answer parameters using machine learning models, allowing the system to serve itself rather than requiring customers to manually provide all parameters. This reduces the burden on customers while maintaining data quality.
Solution Approach 2:
The patent introduces an intermediary prediction system that acts as a mediator between the customer's partial inputs and the complete parameter set required for simulation. The prediction model fills in the gaps automatically, bridging the gap between customer input and simulation requirements.
2Reliability
If advisers help customers fill in the answer sheet, then accurate answers can be obtained, but the process becomes more complex and time-consuming
Solution Approach 1:
The system replaces the need for external advisers by implementing automated prediction capabilities that independently fill in missing parameters. The machine learning model acts as an autonomous expert system that eliminates the need for human intervention in data collection.
Solution Approach 2:
The patent substitutes the mechanical process of adviser-customer interaction with an automated computational system. The machine learning model processes and predicts parameters algorithmically, replacing the human adviser's role in filling out the answer sheet.
3Reliability
If traditional simulation systems require complete parameter input before simulation, then accurate simulation results can be obtained, but the user experience is degraded due to the large number of required inputs
Solution Approach 1:
The system performs preliminary predictions of missing parameters in real-time as the user inputs data, rather than requiring all inputs to be complete before any processing can occur. This allows the simulation to proceed with predicted values, improving productivity while maintaining result accuracy.
Solution Approach 2:
The patent implements a dynamic system where the prediction model continuously updates as new user inputs become available. The system adapts in real-time, adjusting predictions based on newly provided information, rather than requiring static completion of all fields beforehand.
Data Source
AI summary
A method is presented for predicting values of multiple input items. The method includes allowing a user to select a first set of variables and input first values therein and predicting second values for a second set of variables, the second values predicted in real-time as the first values are being inputted by the user. A tree-based prediction model is used to predict the second values. The tree-based prediction model is a regression tree or a decision tree.