Sap Se

Surendranath Gopinathan

Nithya Rajagopalan

Abhishek Chaturvedi

Sunil Gornalle

Gurudayal Khosla

Prasad Karani

Panish Ramakrishna

Jeremiah Reeves

Erik Scheithauer

Samhith Bharadwaj

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

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

In an example embodiment, a first payload in a first electronic communication is received, the first payload containing localization information regarding a first node in a taxonomy of objects, categories, and characteristics. The first payload is parsed to identify one or more dependencies. For each of the one or more dependencies, a dummy node in the taxonomy corresponding to the dependency is created, the dummy node containing information about the dependency from the first payload. A search mechanism is provided to a graphical user interface, the search mechanism designed to receive user interactions from a user and cause one or more hardware processors to retrieve and cause display of information in one or more of the dummy nodes in the taxonomy.

Object search on partial data received from external system

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Bengaluru, India

Surendranath Gopinathan