公开(公告)号：US12299403B2

公开(公告)日：2025-05-13

申请号：US17969111

申请日：2022-10-19

Applicant: SAP SE

Inventor： Prajesh K ,  Prateek Bajaj

IPC: G06F40/40 ,  G06F40/295 ,  G06F40/30

Abstract: Example methods and systems are directed to determining topics of data objects. A machine learning model may be trained and used to determine topics of data objects. After topics for data objects are determined by the trained machine learning model, data objects having similar topics can be automatically related. A semantic web approach relies upon the metadata of the data objects being generated along with the metadata of the insights being generated (such as topic groups). Such a semantic association between various objects (using metadata) forms a metadata driven network of analytical representation of business entities/objects. A data-stream comprising the semantic web, indicating the relationships between the metadata of the data objects and the metadata for the topics, may be pushed continuously into a central tool or repository to allow users to generate seamless analytical dashboards with minimal efforts.

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.

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：US20230153340A1

公开(公告)日：2023-05-18

申请号：US17529706

申请日：2021-11-18

Applicant: SAP SE

Inventor： Prajesh K ,  Somanathan Ramanathan ,  Prateek Bajaj

IPC: G06F16/34 ,  G06F16/2452 ,  G06Q10/06 ,  G06N20/20

CPC classification number: G06F16/345 ,  G06F16/24522 ,  G06N20/20 ,  G06Q10/0633

Abstract: Interactions between organizations occur through multiple channels such as textual communication (e.g., emails) and voice communication (e.g., telephone conversations). All such interaction data collated together constitutes a large amount of unstructured data. A framework is provided for collating the unstructured interaction data and creating a machine-legible structure from it using machine learning models. The machine learning models may generate a variety of generic as well as business-context-relevant insights, with the usage and application of custom-built machine learning model pipelines that generate an overall business insight record that can then be published back into a customer relationship management (CRM) system. Multiple data types are used for the interactions. For example, a voice call may be recorded and stored as an audio file, whereas an email may be stored as a text file. Multiple such formats may also be used to store interaction data.

公开(公告)号：US10229440B2

公开(公告)日：2019-03-12

申请号：US14320385

申请日：2014-06-30

Applicant: SAP SE

Inventor： Prajesh K ,  Baris Yalcin

IPC: G06Q30/02

Abstract: A price calculator may receive a pricing request for at least one item, the at least one item priced in accordance with a pricing schema including a plurality of price components. The price calculator may include a parallel price calculator configured to retrieve a parallel calculation flow handling procedure corresponding to the at least one item and designating at least two price subcomponents common to the price components. The price calculator may then calculate the at least two price subcomponents for the price components in parallel, using the parallel calculation flow handling procedure.