US 12,379,945 B2
Robotic process automation architectures and processes for hosting, monitoring, and retraining machine learning models
Shashank Shrivastava, Bangalore (IN)
Assigned to UiPath, Inc., New York, NY (US)
Filed by UiPath, Inc., New York, NY (US)
Filed on Jan. 7, 2021, as Appl. No. 17/143,392.
Claims priority of application No. 202011051236 (IN), filed on Nov. 25, 2020; and application No. 202011051237 (IN), filed on Nov. 25, 2020.
Prior Publication US 2022/0164700 A1, May 26, 2022
Int. Cl. G06F 9/451 (2018.01); G05B 19/04 (2006.01); G06F 8/61 (2018.01); G06F 21/60 (2013.01); G06F 21/62 (2013.01); G06F 30/27 (2020.01); G06N 3/008 (2023.01); G06N 3/10 (2006.01); G06N 20/00 (2019.01)
CPC G06F 9/451 (2018.02) [G05B 19/04 (2013.01); G06F 8/61 (2013.01); G06F 21/602 (2013.01); G06F 21/6218 (2013.01); G06F 30/27 (2020.01); G06N 3/008 (2013.01); G06N 3/10 (2013.01); G06N 3/105 (2013.01); G06N 20/00 (2019.01); G06F 2212/402 (2013.01); G06F 2221/2107 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A system, comprising:
one or more robotic process automation (RPA) robots configured to:
call one or more machine learning (ML) models by respective activities of respective RPA workflows of the one or more RPA robots, the RPA workflows defining a controllable execution order over time and a relationship between a set of activities comprising the respective activities that call the one or more ML models by an expression of the respective activities, at least one activity of the set of activities configured to utilize at least one driver that facilitates interaction between the one or more RPA robots and software of respective computing systems,
receive results from the call to the one or more ML models, and
complete execution of the respective activities that call the one or more ML models; and
an artificial intelligence (AI) center comprising a plurality of physical servers configured to store the one or more ML models and to execute the one or more ML models upon a call from a respective RPA robot of the one or more RPA robots, wherein
the plurality of physical servers are configured to:
store a plurality of datasets for the ML models, each dataset comprising similar types of data in a logical or physical grouping, and
retrain an ML model of the one or more ML models responsive to a training condition being met or responsive to retraining being manually requested using a subset of the plurality of datasets specified in a training configuration for the ML model and deploy the retrained ML model to be called by the one or more RPA robots, wherein the ML model is retrained based on a confidence threshold.