Sap Se

International Business Machines Corporation

Gurudayal Khosla

Nithya Rajagopalan

Abhishek Chaturvedi

Sunil Gornalle

Surendranath Gopinathan

Prasad Karani

Panish Ramakrishna

Shunmugaraja Periadurai

Jeremiah Reeves

Mansi Gaba

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

A method for multiplying an object in a database may include receiving, from a client, an indication that an object is associated with a first variable attribute having a first variation and a second variation. A bucket may be created in response to the indication. The bucket may include the object, a first sub-object having a first reference to the first variation of the first variable attribute, and a second sub-object having a second reference to the second variation of the first variable attribute. The bucket may be created instead of a first object having the first variation of the first variable attribute and a second object having the second variation of the first variable attribute. The bucket stored in the database may be accessed in response to a query from the client accessing the object. Related systems and articles of manufacture including computer program products are also provided.

Multiplication of database objects

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

Stepwise execution of different instances of an application can be initiated, in parallel, in different computing environments. Responsive to an execution step being performed in the different computing environments, a determination can be made as to whether a first data set generated by an operation performed in the execution step by a first instance of the application matches a second data set generated by the operation performed in the execution step by a second instance of the application. If the data sets do not match, the second data set can be replaced with the first data set in memory of an operating system in which the second instance of the application is executed. At least one additional execution step can be performed. An indication of whether the additional execution step performed properly can be output.

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Bengaluru, India

Gurudayal Khosla