Servicenow, Inc.

Jebakumar Mathuram Santhosam Swvigaradoss

Ankit Goel

Prashant Pandey

Manjeet Singh

Rajesh Voleti

Rishabh Verma

Satya Sarika Sunkara

John Alan Botica

Fabio Casati

Laxmi Prasanna Mustala

Sakshi Kataria

Samantha Santo

Sarthak Jain

Sharfuddin Mohammed

Daniel Grady

An embodiment may involve obtaining a log regarding execution of a software application; obtaining indications of availabilities of resources related to the software application; determining, from the log and the indications of availabilities of the resources, a time series of software application activities; and training a prediction engine with the time series of software application activities, wherein the prediction engine as trained is configured to receive an input time series of further software application activities and generate an output time series that predicts additional software application activities. Another embodiment may involve obtaining an input time series of software application activities, wherein the input time series is based on a log regarding execution of a software application and includes indications of availabilities of resources associated with the software applications; and generating, using a prediction engine, an output time series based on the input time series that predicts additional software application activities.

Discovery and predictive simulation of software-based processes

An embodiment may involve a database containing a first user profile that specifies a first preferred language of a first user and a second user profile that specifies a second preferred language of a second user. The embodiment may also involve one or more processors configured to: receive, from the first user and within a chat session, a first set of messages in the first preferred language; cause the first set of messages to be translated into the second preferred language; provide, to the second user and within the chat session, the first set of messages as translated; receive, from the second user and within the chat session, a second set of messages in the second preferred language; cause the second set of messages to be translated into the first preferred language; and provide, to the first user and within the chat session, the second set of messages as translated.

Machine translation of chat sessions

An embodiment includes storage containing configuration and operational data related to a managed network, an action configurator application, and an action execution application. The embodiment also includes one or more processors configured to: receive, by way of the action configurator application, a specification of an action type; receive, by way of the action configurator application, a specification of an input source for actions generated using the action type; receive, by way of the action configurator application, a specification of one or more target users and an output modality for the actions; receive, by way of the action configurator application, a specification of a condition that causes the actions to be generated; determine, that the condition has been satisfied; and cause the action execution application to execute the action generator on the input source, and produce an output for one of the target users by way of the output modality.

Configurable action generation for a remote network management platform

An example embodiment may involve a software application executable on computing devices of a remote network management platform containing a computational instance associated with a managed network. A text string may be received, and characters of the string may be categorized among a plurality of symbol script families. A respective likelihood of the string corresponding to each family may be determined, and a respective probability of the string being in each language of each given family may also be determined. The respective probabilities for the languages of each given family may be weighted by the likelihoods of the given family, and then weighted sums of the probabilities for each language may be computed. The maximum of the weighted sums may correspond to the language of the text string. The respective probabilities may be determined according to hybrid N-gram and word language models for each family.

Hybrid language detection model

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Hyderabad Telangana, India

Jebakumar Mathuram Santhosam Swvigaradoss