Dual-Model Algorithm for Relocation Cost Estimation
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Solution Overview
Problem
Current regression models for estimating relocation costs fail to accurately account for individual prediction uncertainty, as they use a constant uncertainty estimation method that does not vary with covariates, leading to inaccurate budgeting for employers.
Innovation Solution
A dual-model algorithm is employed to generate predictive models that account for varying uncertainty based on covariates, using a combination of target and uncertainty regression models to provide more accurate relocation cost estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a constant uncertainty estimation method is used in regression models, then the model construction is simple, but the accuracy of individual prediction uncertainty is insufficient
Solution Approach 1:
The patent divides the regression model into two separate models: a target model that predicts relocation costs and an uncertainty model that predicts prediction uncertainty. This segmentation allows each model to specialize in its specific function, improving the accuracy of uncertainty estimation without excessively increasing overall model complexity.
Solution Approach 2:
The patent adds a new dimension to the prediction output by introducing uncertainty estimation as a separate predictive dimension. Instead of only predicting relocation costs, the system now predicts both costs and uncertainty, providing employers with confidence intervals for budgeting decisions.
2Measurement precision
If average estimation costs are used based on similar relocations, then the estimation process is simple, but the accuracy fails to account for uncertainty
Solution Approach 1:
The patent pre-trains both the target model and uncertainty model on historical relocation data before actual cost estimation is needed. This preliminary action allows the models to be ready for rapid deployment, providing accurate uncertainty-adjusted estimates without time-consuming calculations during the estimation phase.
Solution Approach 2:
The patent uses historical relocation data as copies of past experiences to train the regression models. By learning from these copied patterns in historical data, the system can quickly generate accurate cost estimates with uncertainty measurements for new relocation scenarios without reprocessing all historical data each time.
3Measurement precision
If a dual-model algorithm is implemented to provide varying uncertainty, then the accuracy of individual predictions improves, but the computational complexity increases
Solution Approach 1:
The dual-model algorithm segments the prediction task into two independent but complementary models: one for target prediction and one for uncertainty prediction. This segmentation simplifies the overall algorithmic complexity by allowing each model to be trained and optimized separately rather than requiring a single complex model to handle both tasks simultaneously.
Solution Approach 2:
The patent introduces an intermediary uncertainty model that acts as a mediator between the target model and the final prediction output. This intermediary provides confidence intervals that help employers interpret the target predictions, improving individual prediction accuracy without requiring the target model itself to become more complex.
Data Source
AI summary
A method for improving the estimation of relocation costs including the steps of: generating a relocation costs data-model; performing a dual-model algorithm on the relocation costs data-model to determine a preliminary relocation costs predictive model for a relocation service; receiving a first dataset of a subject to be relocated; analyzing the first dataset with the preliminary relocation costs predictive model to generate a preliminary relocation costs for the relocation service; displaying, on a display of a remote device, the preliminary relocation costs; performing the dual-model algorithm on the relocation costs data-model to determine a supplemental relocation costs predictive model for the relocation service; receiving a second dataset of the subject to be relocated; analyzing the second dataset with the supplemental relocation costs predictive model to generate a supplemental relocation costs for the relocation service; and displaying, on the display of the remote device, the supplemental estimated relocation costs.