Machine-Learned Model Wrappers for Cost-Aware Input Acquisition
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Solution Overview
Problem
Existing machine-learned models often require a fixed quantity and type of input data, leading to inefficiencies such as excessive data collection for minimal accuracy improvement or insufficient data that could enhance performance, without considering the varying costs and benefits of additional data.
Innovation Solution
A method for interactively acquiring input data by estimating the informational value of additional inputs using a machine-learned model, determining the need for and type of additional data based on estimated information gain, and utilizing a wrapper system that does not require retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine-learned model is configured to process a fixed quantity and type of input data, then the model can operate with consistent performance expectations, but the system may collect too much data (high-cost data having little impact on model accuracy) or process too little data (ignoring low-cost input data that could improve performance)
Solution Approach 1:
The patent implements dynamic input data acquisition by estimating the informational value of additional inputs based on already-obtained inputs. The system interactively determines whether to acquire more data and what type of data to acquire, adjusting the quantity and type of inputs dynamically rather than using a fixed quantity. This resolves the contradiction by enabling the system to collect only the necessary amount of data to achieve sufficient accuracy, avoiding both over-collection and under-collection of data.
Solution Approach 2:
The patent changes the parameter of input data quantity from fixed to variable based on estimated informational value. By calculating the expected improvement in accuracy from additional inputs and comparing it to acquisition costs, the system adjusts the data quantity parameter dynamically. This allows the system to optimize the balance between data collection costs and accuracy improvements, resolving the technical contradiction between precision and quantity.
2Measurement precision
If additional input data is obtained to improve model performance, then inference accuracy may improve, but data acquisition costs increase
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously estimates the informational value of additional inputs based on already-obtained inputs and uses this estimation to determine whether to acquire more data. The estimated informational value serves as feedback that guides the data acquisition process, allowing the system to stop collecting data when the expected accuracy improvement no longer justifies the acquisition cost. This feedback loop resolves the contradiction by preventing unnecessary data collection that would increase costs without providing proportional accuracy benefits.
Solution Approach 2:
The patent performs preliminary estimation of the informational value of additional inputs before actually acquiring them. By calculating the expected improvement in accuracy and comparing it to the acquisition cost in advance, the system can make informed decisions about whether to proceed with data collection. This preliminary action allows the system to avoid costly data acquisition when the expected benefit is insufficient, resolving the contradiction between accuracy improvement and cost reduction.
3Adaptability or versatility
If a wrapper system is used to interactively acquire input data without retraining the model, then existing models can be utilized, but the system complexity increases
Solution Approach 1:
The patent introduces a wrapper system as an intermediary layer between the existing machine-learned model and the data acquisition process. This wrapper estimates the informational value of additional inputs, determines whether to acquire more data, and selects what data to acquire, while the underlying model remains unchanged and reusable. The wrapper acts as a mediator that adds interactive data acquisition capability without requiring model retraining, resolving the contradiction by enabling model reusability while managing system complexity through a dedicated intermediary component.
Data Source
AI summary
Systems and methods are provided for wrapping a machine-learned model to facilitate interactive input acquisition. One or more computing devices can obtain a machine-learned model configured to generate a prediction based at least in part on input feature data. The computing device(s) can obtain a first input value for a first input feature of the first machine-learned model. Based at least in part on the first input value, the computing device(s) can determine an estimated value of obtaining at least one additional input value for a second input feature of the first machine-learned model. Based on the estimated value, the computing device(s) can determine whether to obtain the at least one additional input value. Using the first machine-learned model, the computing device(s) can determine a prediction based at least on the first input value.