Abstract: A received query is parsed by a first machine learning (ML) model to retrieve one or more semantic entities. Next, a second ML model is trained with a list of application programming interfaces (APIs) and corresponding first data to generate a second trained ML model which receives the one or more semantic entities as inputs. Based on these inputs, the second trained ML model selects a given API from the list of APIs. Then, a third ML model is trained with the given API and corresponding second metadata to generate a third trained ML model. Next, the third trained ML model receives the one or more semantic entities as inputs and generates a given API call for invoking the given API. Then, the given API call is executed and the query is completed to a first computing system based on invoking the given API.

Abstract: A business object model, which reflects data that is used during a given business transaction, is utilized to generate interfaces. This business object model facilitates commercial transactions by providing consistent interfaces that are suitable for use across industries, across businesses, and across different departments within a business during a business transaction. In some operations, software creates, updates, or otherwise processes information related to an entitlement product business object.

Abstract: A business object model, which reflects data that is used during a given business transaction, is utilized to generate interfaces. This business object model facilitates commercial transactions by providing consistent interfaces that are suitable for use across industries, across businesses, and across different departments within a business during a business transaction. In some operations, software creates, updates, or otherwise processes information related to an entitlement product business object.