公开(公告)号：US20230134277A1

公开(公告)日：2023-05-04

申请号：US17553432

申请日：2021-12-16

Applicant: SAP SE

Inventor： Umesh K ,  Jovin Jijo ,  Anirudh Prasad ,  Mohit V. Gadkari ,  Christian Weiss

IPC: G06F11/36

Abstract: A method for secure debugging in a multitenant cloud environment where an application server maintains a host application shared by multiple tenant users can be implemented. The method can receive a request from a tenant user to debug the host application associated with a tenant user, and responsive to the request, deploy an application runtime environment comprising an application container encapsulating the host application associated with the tenant user and a debugger container encapsulating a debugging software running on the application server. The method can set at least a breakpoint in source code of the host application through a user interface of the debugging software, run the host application associated with the tenant user in the application runtime environment, and evaluate an expression entered through the user interface of the debugging software after the host application associated with the tenant user hits the breakpoint.

公开(公告)号：US20240104424A1

公开(公告)日：2024-03-28

申请号：US17952107

申请日：2022-09-23

Applicant: SAP SE

Inventor： Mohit V. Gadkari ,  Ankur Malik ,  Sunil S. Parvatikar ,  Simona Marincei ,  Dalibor Knis ,  Anirudh Prasad ,  Kopal Jauhari ,  Saurabh Saxena ,  Yatish Nagaraja ,  Pankaj Kumar Agrawal ,  Long Qian ,  Varun Verma

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The present disclosure involves systems, software, and computer implemented methods for an artificial intelligence work center for ERP data. One example method includes receiving scenario and model settings for an artificial intelligence model for a predictive scenario. A copy of the dataset is processed based on settings to generate a prepared dataset that is provided with the settings to a predictive analytical library. A trained model trained and evaluation data for the trained model is received from the predictive analytical library. A request is received to generate a prediction for the predictive scenario for a target field for a record of the dataset. The record of the dataset is provided to the trained model and a prediction for the target field for the record the dataset is received from the model. The prediction is included for presentation in a user interface that displays information from the dataset.