Sap Se

Samsung Electronics Co., Ltd.

Abhishek Chaturvedi

Nithya Rajagopalan

Sunil Gornalle

Gurudayal Khosla

Surendranath Gopinathan

Prasad Karani

Panish Ramakrishna

Jeremiah Reeves

Jajohn Mathew Mattam

Manasi Ekkundi

Varadarajan Seenivasan

Erik Scheithauer

Samhith Bharadwaj

Girija B

Jehil Vora

Abhishek Shyamsundar Bhattad

The embodiments herein provide a method for reducing KPI data. The disclosed technique is used to reduce insignificant KPI data generated by network entities (NEs) such as Radio Access Network (RAN) or element management system (EMS)/network management system (NMS). Further, the method includes determining a threshold value for each KPI data to determine significance of the KPI data and uses the threshold value to reduce the KPI data in a file. Further, the method includes utilizing a network bandwidth (e.g., control plane traffic load) and a storage space in the EMS/operations support system (OSS) efficiently and helps operators of the EMS/OSS to identify a network KPI deterioration in near real time.

Method and network entity for handling KPI PM data

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

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

In an embodiment, a method, implemented in a User Equipment (UE) operating in Evolved Universal Terrestrial Radio Access Network (E-UTRAN) New Radio-Dual Connectivity (EN-DC), of adding a New Radio (NR) Secondary Node (SN) in EN-DC is disclosed. The method includes receiving, in response to a measurement report sent by the UE, configuration data associated with an NR cell. The method includes, ascertaining whether the configuration data comprises a pre-determined parameter indicative of a frequency-shift for dynamic spectrum sharing for an uplink. The method includes performing one of an addition of an NR SN corresponding to the NR cell or a transmission of a failure notification message to the MN, based upon determining that the pre-determined parameter indicative of the frequency-shift criteria for dynamic spectrum sharing for the uplink is absent or present in the configuration data.

Systems and methods for UE to avoid issues in dynamic spectrum sharing

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.

Growing community of inventors

Bengaluru, India

Abhishek Chaturvedi