Servicenow, Inc.

Fernando Ros

Baskar Jayaraman

Kannan Govindarajan

Aniruddha Madhusudan Thakur

Sriram Palapudi

Nikhil Prashant Bendre

Firat Karakusoglu

Khosrow Jian Motamedi

Nikhil Bendre

Carmine Mangione-Tran

Frank Wall Elliott, Jr

Douglas Andrew Bell

Veera Sekhar Babu Golla

Niranjan Samanu

Rakesh Kumar Gyanchandani

Gregory Allen Krasnow

A request associated with access to a restricted computer resource by a computer application of a device is received via a first communication medium. It is determined that the request is provided by the device with an IP address not included in a group of authorized IP addresses. A registration secret is generated. A representation associated with the registration secret is provided via a second communication medium. A token signed using the registration secret is received. In response to successfully validating the token, a communication secret is generated and associated with an identifier associated with the device. The communication secret is provided for use by the computer application of the device to access the restricted computer resource.

Computer access control using registration and communication secrets

A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Machine learning with distributed training

A system includes a processor configured to create a rule repository instance. The rule repository instance specifies a plurality of different procedures that facilitate specifying a sequence of transformer rules by cascading each of the procedures together using a dot notation format. The processor configures the rule repository instance with a plurality of transformer rules using the dot notation format. The processor receives data from a file arranged according to a first structured data format. The processor executes the sequence of transformer rules to convert data elements in the file to a second structured data format. The processor then provides for display or storage the data as converted into the second structured data format by the sequence of transformer rules.

Unified application programming interface for transformation of structured data

A system includes a processor configured to obtain a sequence of transformer rules. The transformer rules specify a set of data elements arranged according to a first structured data format, and structural changes to be performed on the data elements that convert the data elements into a second structured data format. The processor receives a block of data from a file arranged according to the first structured data format. The processor executes the sequence of transformer rules to perform the structural changes to the block of data. When executing the particular transformer rule, the processor applies an adapter associated with the transformer rule to modify values in the block of data specified by the particular transformer. The processor then provides for display or storage the block of data as converted into the second structured data format by the sequence of transformer rules.

Memory-efficient programmatic transformation of structured data

A method includes receiving, at a processor, ticket data representing a ticket. The method further includes receiving, at the processor, description data representing a description of the ticket. The method further includes calculating, based on the description data, a first probability that the ticket corresponds to a first category and a second probability that the ticket corresponds to a second category. The method further includes determining an entropy value associated with routing the ticket data. The method further includes, in response to the entropy value satisfying a threshold and the first probability exceeding the second probability, routing the ticket data to a device associated with the first category.

Ticket routing

Shared machine learning

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Carlsbad, CA, United States of America

Fernando Ros