Paypal, Inc.

Rajendra Bhat

Prabin Patodia

Joydeep Hazra

Shivam Jari

Santosh Bharamasagar Chandrasekharappa

Md Faiz Alam

Sourav Kumar

Harshith Thonupunoori

Vijayent Kohli

Various techniques are disclosed for providing gateway services between a client system and downstream service systems for a service system. The disclosed gateway service system is capable of providing a final decision response to a service request from the client system based on responses received from the downstream service systems. The gateway service system internally determines the final decision response through implementation of a decision determination processor in combination with a configuration file and a response mapping table. The configuration file provides information that allows the decision determination processor to determine the final decision response based on decision mapping in the response mapping table. In some instances, the decision determination processor may access a dynamic decision mapping model when the response mapping table does not provide a final decision response determination.

Gateway service decision process consolidation

A service provider may create data specification-driven AI-based executable strategies, which may be used to ensure high uptime and availability of decision services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making. However, at certain times, the decision service may fail or the services and/or a gateway for such services may be inaccessible. To provide higher availability and better SLA times, a client-side executable strategy for decision service execution may be determined using the pathways for strategy execution and available data from called resources. This strategy may be loaded in parallel to calling the decision service, and when failure occurs, may be used as a fallback to request processing.

Dynamic creation of data specification-driven AI-based executable strategies for high availability of evaluation services

There are provided systems and methods for configuration-driven efficient transformation of formats and object structures for data specifications in computing services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. Different services may process data in different data formats and structures. However, transformation of data between different services, such as a gateway service that may receive data processing requests and/or data objects and downstream services that may process such requests and objects, may take significant time and resources. A configuration-driven data transformation platform may intelligently create code for and select from transformers that may be used for data transformations. When selected, the transformers may transform data between services faster and more efficiently by being specifically selected based on past performances and code configurations.

Configuration-driven efficient transformation of formats and object structures for data specifications in computing services

There are provided systems and methods for an automatically managed common asset validation framework for platform-based microservices. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making from input data including data in a production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations. In this regard, the decision services may share common data assets, such as data tables, shared code for execution of operations and the like. The service provider may utilize an intelligent service to automatically manage and validate these assets between services so that execution errors do not occur when there are changes to these common assets.

Automatically managed common asset validation framework for platform-based microservices

There are provided systems and methods for increasing availability of a micro-computation decision service by utilizing execution flow configurations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations.

Increasing availability of a micro-computation decision service by utilizing execution flow configurations

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.

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Bengaluru, India

Rajendra Bhat