Sap Se

Prajesh K

Prateek Bajaj

Baris Yalcin

Jing Ye

Yann Le Biannic

Prerna Makanawala

Karthik S J

Arjun Karat

Xiaoqing He

Amy He

Georg Glantschnig

Subhobrata Dey

Somanathan Ramanathan

Riya Thosar

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.

Machine learning for multi-channel interaction workflows

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.

Intelligent entity relation detection

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.

Applying scoring systems using an auto-machine learning classification approach

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.

Accelerated price calculation using in-memory technology

A system generates a plurality of price master data lookup procedures that are capable of being executed in parallel. The plurality of price master data lookup procedures is stored in a repository in an in-memory system. The in-memory system includes a parallel processor. The system accesses the repository of price master data lookup procedures, and retrieves price master data in parallel using the price master data lookup procedures. The system then calculates a price using the price master data retrieved from the price master database in the in-system memory.

System for accelerated price master database lookup

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Mattanur, India

Prajesh K