US 12,443,621 B1
Using reinforcement learning to recommend data visualizations
Vibhor Porwal, Bidhuna (IN); Subrata Mitra, Bangalore (IN); Shubham Agarwal, West Bengal (IN); Ryan A Rossi, Santa Clara, CA (US); Ghazi Shazan Ahmad, West Bengal (IN); Manav Ketan Doshi, Maharashtra (IN); and Syam Manoj Kumar Paila, Andhra Pradesh (IN)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by ADOBE INC.
Filed on May 20, 2024, as Appl. No. 18/668,888.
Int. Cl. G06F 16/26 (2019.01)
CPC G06F 16/26 (2019.01) 20 Claims
OG exemplary drawing
 
1. One or more non-transitory computer-readable media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising:
determining, based on applying a sample of a dataset to a data visualization recommendation model, statistical features of the sample for generating data visualizations of the sample;
determining, based on applying the statistical features and the dataset to a regression model, corresponding computational costs of the statistical features when applying the statistical features to the dataset;
determining, based on applying a computational budget, the sample of the dataset and the corresponding computational costs to a reinforcement learning model, recommended statistical features by causing the reinforcement learning model to:
select corresponding statistical features from the statistical features of the sample to add to the recommended statistical features until the computational budget is met by determining, based on a reward function, whether to explore a statistical feature from the statistical features of the sample or exploit a previously explored statistical feature from the statistical features of the sample; and
minimize a loss function between the recommended statistical features and the statistical features of the sample until the loss function is within a threshold value; and
causing display, based on applying the dataset and the recommended statistical features to the data visualization recommendation model, of a recommended data visualization.