公开(公告)号：US12210937B2

公开(公告)日：2025-01-28

申请号：US16194157

申请日：2018-11-16

Applicant: SAP SE

Inventor： Karthik S J ,  Amy He ,  Prajesh K ,  Georg Glantschnig ,  Riya Thosar ,  Arjun Karat ,  Yann Le Biannic ,  Jing Ye ,  Subhobrata Dey ,  Prerna Makanawala ,  Xiaoqing He

IPC: G06N20/00 ,  G06F9/445 ,  G06F16/215 ,  G06N20/20

Abstract: A method of improving usability and transparency of machine-learning aspects of applications providing various types of services is disclosed. Based on a request submitted through an administrative user interface, a data readiness check is performed on underlying data associated with the application. Based on a successful completion of the data readiness check, a configuration file is retrieved from an application server. The configuration file specifies a plurality of keys for generating a machine-learned model for the application. The machine-learned model is trained based on the plurality of keys specified in the configuration file. The machine-learned model is selected from a plurality of machine-learned models based on dry runs of the each of the plurality of models. The machine-learned model is activated with respect to the application. Scores are identified from the underlying data items based on the selected machine-learned model.

公开(公告)号：US20200159690A1

公开(公告)日：2020-05-21

申请号：US16194157

申请日：2018-11-16

Applicant: SAP SE

Inventor： Karthik S. J ,  Amy He ,  Prajesh K ,  Georg Glantschnig ,  Riya Thosar ,  Arjun Karat ,  Yann Le Biannic ,  Jing Ye ,  Subhobrata Dey ,  Prerna Makanawala ,  Xiaoqing He

IPC: G06F15/18 ,  G06K9/62 ,  G06F9/445

Abstract: A method of improving usability and transparency of machine-learning aspects of applications providing various types of services is disclosed. Based on a request submitted through an administrative user interface, a data readiness check is performed on underlying data associated with the application. Based on a successful completion of the data readiness check, a configuration file is retrieved from an application server. The configuration file specifies a plurality of keys for generating a machine-learned model for the application. The machine-learned model is trained based on the plurality of keys specified in the configuration file. The machine-learned model is selected from a plurality of machine-learned models based on dry runs of the each of the plurality of models. The machine-learned model is activated with respect to the application. Scores are identified from the underlying data items based on the selected machine-learned model.