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AssigneeUncountable Inc.
InventorsJason Isaac Hirshman, Noel Hollingsworth, Will Tashman
Estimating the term…
Recommendations for new experiments are generated via a pipeline that includes a predictive model and a preference procedure. In one example, a definition of a development task includes experiment parameters that may be varied, the outcomes of interest and the desired goals or specifications. Existing experimental data is used by machine learning algorithms to train a predictive model. The software system generates candidate experiments and uses the trained predictive model to predict the outcomes of the candidate experiments based on their parameters. A merit function (referred to as a preference function) is calculated for the candidate experiments. The preference function is a function of the experiment parameters and/or the predicted outcomes. It may also be a function of features that are derived from these quantities. The candidate experiments are ranked based on the preference function.
A method implemented on a computer system comprising a processor, the processor executing instructions to effect a method for recommending candidate experiments, the method comprising
A non-transitory computer-readable storage medium storing executable computer program instructions to effect a method for recommending candidate experiments, the instructions executable by a processor and causing the processor to perform a method comprising:
A system for recommending candidate experiments, the system comprising:
Dependencies are inferred from the claim text.