Paypal, Inc.

Joydeep Hazra

Srinivasan Manoharan

Shivam Pandey

Rajendra Bhat

Md Faiz Alam

Vijayent Kohli

Santosh Bharamasagar Chandrasekharappa

Harshith Thonupunoori

Sourav Kumar

There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Fallback artificial intelligence system for redundancy during system failover

A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Optimizing timeout settings for nodes in a workflow

Growing community of inventors

Bengaluru, India

Joydeep Hazra