Sap Se

Nithya Rajagopalan

Panish Ramakrishna

Abhishek Chaturvedi

Gurudayal Khosla

Sunil Gornalle

Nishant Kumar

Surendranath Gopinathan

Kumaraswamy Gowda

Rajendra Vuppala

Erica Vandenhoek

Prasad Karani

Vidhya Neelakantan

Jeremiah Reeves

Ashutosh Patel

Erik Scheithauer

Debashis Banerjee

Hari Babu Krishnan

Prasanna Kumar Govindappa

Krishna Hindhupur Vijay Sudheendra

Avinash Permude

Rahul Seshagiri Srivatsa

Kiran Prashanth Kulkarni

Mudit Joshi

Samhith Bharadwaj

Avneesh Sharma

Girija B

Jehil Vora

Some embodiments provide a program that determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The program further sorts values of the first attribute of the plurality of data objects. The program also sorts values of the second attribute of the plurality of data objects. The program further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The program also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The program further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.

Clustering of data objects based on data object attributes

Various examples are directed to determining an impact of a subunit on the value of a unit. A web-based analytics system receives unit value data via a user interface page provided to a supplier computing device by a web application, the unit value data including relationship data describing a relationship between a plurality of subunit values and a unit value. Based on relationship data, the system increases a value for a first subunit while holding values for the other subunits constant until the value of the unit increases to a unit value threshold and increases a value for a second subunit while holding values for the other subunits constant until the value of the unit increases to a unit value threshold. The system compares an increase in the value of the first subunit to increase the value of the unit to the unit value threshold with an increase in the value of the second subunit to increase the value of the unit to the unit value threshold. Based on the comparing, the system determines that the first subunit is a more significant driver of the value of the unit than the second subunit.

Web-based system and method for unit value driver operations

In an example embodiment, a request is received, via a graphical user interface, to add a new object to a directory of objects, the new object having a first category in a hierarchical taxonomy of categories and objects. Then one or more questions previously assigned to the first category and/or one or more existing objects within the first category are retrieved. Each of the retrieved one or more questions and information about the new object are then fed into a first machine learned model trained to output a probability that a question is applicable to an object. One or more questions are generated for the new object based on the probability for each of the retrieved one or more questions. At least one of the one or more generated questions is then assigned to the new object.

Dynamic question recommendation

Various examples are directed to systems and methods for generating a contract form using a contract library data structure. A computing device may receive, from a user, a contract form request comprising a set of contract classifiers. The computing device may access a contract library data structure and executes a filter routine to traverse the contract library data structure to identify at least one contract term associated with one or more filter tags corresponding to the set of contract classifiers. The filter routine may return an indication of the first contract term associated with the first node. The computing device may generate a contract form comprising the first contract term.

Contract library data structures and uses thereof

A method may include receiving, at a recommendation engine, a first indication to create a first sourcing event that includes a first object and a second object. The recommendation engine may respond to the first indication by updating a learning model to increment a first frequency of the first object being included in a sourcing event, a second frequency of the second object being included in a sourcing event, and/or a third frequency of the first object and the second object being included simultaneously in a sourcing event. The recommendation engine may receive a second indication to create a second sourcing event. In response to the second indication, the recommendation engine may apply the learning model to generate a recommendation to add, to the second sourcing event, the first object instead of the second object. Related systems and articles of manufacture are also provided.

Learning model based recommendation engine

The present disclosure involves systems, software, and computer implemented methods for creating line item information from tabular data. One example method includes receiving event data values at a system. Column headers of columns in the event data values are identified. At least one column header is not included in standard line item terms used by the system. Column values of the columns in the event data values are identified. The identified column headers and the identified column values are processed using one or more models to map each column to a standard line item term used by the system. The processing includes using context determination and content recognition to identify standard line item terms. An event is created in the system, including the creation of line items from the identified column value. Each line item includes standard line item terms mapped to the columns.

Creating line item information from free-form tabular data

Data is received that specifies a term generated by user input in a graphical user interface. Thereafter, the term is looked up in a dictionary in which there are multiple classes for terms. The term can be classified based on a first class having a top ranked effective count for the term within the dictionary when a ratio of the first class relative to a second class having a second ranked effective count for the term in the dictionary is above a pre-defined threshold. In addition, the term is classified using a machine learning model when the ratio of the first class relative to the second class is below the pre-defined threshold. Data can be provided which characterizes the classifying. Related apparatus, systems, techniques and articles are also described.

Multi-stage adaptable continuous learning / feedback system for machine learning models

User-generated input is received that includes a sequence of words associated with initiation of a computer-implemented event. Thereafter, such input is parsed using at least one natural language processing (NLP) model. This parsed input is then used by a machine learning model to determine a suggested template having a plurality of fields for initiating the event. The template can then be presented in a graphical user interface. Related apparatus, systems, techniques and articles are also described.

Sourcing object creation from natural language inputs

Aspects of the current subject matter are directed to implementing a distribution scenario in a system. In particular, implementations of the current subject matter provide for a designating client device to create a group of distributing client devices for events among and with a plurality of second client devices. Implementations of the current subject matter further relate to automatic assignment of items among the group of distributing client devices, the assignment based on designating client device-established constraints, and to providing an aggregate view of information related to the automatic assignment of the items to allow the designating client device to manage the items.

Intelligent cosourcing in an e-procurement system

Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.

Database record searching with multi-tier queries

In one aspect, there is provided a method including presenting a first user interface including an object hierarchy comprising a parent node and a plurality of child nodes; receiving, at a second user interface, a first selection allocating at first portion of the object hierarchy to a first device; receiving, at the second user interface, a second selection allocating a second portion of the object hierarchy to a second device; and when a selection at a user interface element of a third user interface is received, sending an indication to a system to enable generation of a fourth user interface presenting a first electronic document for the first device and generation of a fifth user interface presenting a second electronic document for the second device. Related systems, methods, and articles of manufacture are also disclosed.

Splitting a task hierarchy

A method of processing raw data as it is received from a data provider via an input channel is disclosed. Values are derived from the raw data as it is received from the data provider via the input channel. The derived values correspond to custom fields of a summary table. The summary table is configured to store a summary of the raw data. The custom fields correspond to data capable of improving an analysis of an entity by an analysis tool. The derived values are inserted into the custom fields of the summary table. Access to the summary table is provided as enriched data for use by the analysis tool to improve the analysis of the entity.

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Bengaluru, India

Nithya Rajagopalan