Multitask Process Effect Estimation for Optimal Treatment Combinations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating individual process effects are limited to single time points and do not account for multiple process types, making it difficult to determine the optimal quantity of processes to achieve the highest effect.
Innovation Solution
An information processing device that generates supervised data including causal variables and process types, and uses a multitask Gaussian process to learn and estimate outcome variables for multiple processes, considering factors like attributes, purchase history, and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional single-time-point estimation methods are used, then the estimation process is simple, but the ability to determine optimal process combinations and quantities is insufficient
Solution Approach 1:
The patent extends the estimation from single time points to multiple time points, adding a temporal dimension. This allows the system to capture dynamic changes in treatment effects over time, enabling determination of optimal process combinations and quantities while maintaining a structured estimation framework.
Solution Approach 2:
The patent creates a unified estimation framework that handles multiple process types (treatment, control, and their combinations) simultaneously. The learning model is designed to estimate individual treatment effects, control effects, and their interactions in a single integrated system, providing versatile optimization capabilities.
2Adaptability or versatility
If multiple process types are considered, then the ability to optimize process combinations improves, but the complexity of data generation and model learning increases
Solution Approach 1:
The patent segments the complex estimation problem into distinct components: treatment effects, control effects, and their interactions. By generating supervised data for each component separately and then combining them in the learning model, the system handles multiple process types systematically while managing data generation complexity.
Solution Approach 2:
The patent introduces process quantity as a variable parameter that can be optimized. The learning model estimates effects as functions of process quantities, allowing the system to determine optimal process combinations and quantities by varying these parameters and evaluating their impact on treatment outcomes.
3Ease of operation
If individual treatment effects are estimated at single time points, then the estimation method is straightforward, but the degree of process quantity cannot be considered
Solution Approach 1:
The patent transitions from static single-time-point estimation to dynamic multi-time-point estimation. The learning model captures how treatment effects evolve over time and vary with process quantities, preserving information about temporal dynamics and quantity-dependent effects while maintaining operational simplicity through automated model learning.
Data Source
AI summary
An estimation device includes an input unit that generates supervised data including causal variables, process types, and outcome variable for each of multiple processes, and a training unit that uses the supervised data to generate a learning model by learning the outcome variables from the causal variables and the process types for each of the processes.