Servicenow, Inc.

Prabodh Saha

Manojkumar Haridas Shende

Amitav Mohanty

Sreenivas Durvasula

Venu Gopal Rao Vajjala

Amit Kumar Mall

Jonathan Sparks

Venkata Satya Sai Rama Murthy Manda

Nitin Chugh

Tirumala Reddy Annem

Revanth Chowdary

Venkata Naga Manoj Barthipudi

Matthew Kilbride

Jean-Remy Facq

Kevin Pickard

Shambhavi Meenakshi Kamakshivalli

Machine generated data entries are received. The machine generated data entries are clustered into a plurality of different clusters that each includes a different subset of the received machine generated data entries. For each of the plurality of different clusters, content of the corresponding subset of the received machine generated data entries belonging to the corresponding cluster of the plurality of different clusters is analyzed to determine a corresponding pattern of the corresponding cluster. A new machine generated data entry is received. The new machine generated data entry is matched to one of the determined patterns. The new machine generated data entry is assigned to one of the plurality of different clusters corresponding to the matched pattern.

Matching machine generated data entries to pattern clusters

The present approach relates to systems and methods for facilitating an interrupt or change in automated tasks in progress, and using a control token (e.g., flag) to modify the progress of the automated task. In contrast to current automated tasks that may not be modified once the request for the automated task has been sent, the presently disclosed techniques allow for controlling and/or changing an automated task by using the control token and an associated status in conjunction with logical checkpoints defined for a thread at which the thread can be paused and the status changed.

Control token and hierarchical dynamic control

The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Lock scheduling using machine learning

The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information. In this manner, the feedback framework provides sufficient context information associated with the feedback to diagnose and address issues while delivering the feedback and context information quickly and efficiently.

Feedback framework

A system may involve a communication bus and computational instances configured for communication with one another by way of the communication bus. The system is configured to: (i) receive, by a first computational instance, a first communication request, wherein the first communication request specifies first attributes; (ii) store, in a first persistent storage, the first attributes; (iii) generate a second communication request that specifies second attributes; (iv) transmit, by the first computational instance and to a second computational instance by way of the communication bus, the second communication request; (v) receive, by the second computational instance, the second communication request; (vi) store, in a second persistent storage, the second attributes; (vii) generate a third communication request that specifies third attributes; and (viii) transmit, by the second computational instance and to a third computational instance by way of the communication bus, the third communication request.

Integrated operational communications between computational instances of a remote network management platform

A computing system includes an anonymizer server. The anonymizer server is communicatively coupled to a data repository configured to store a personal identification information (PII) data. The anonymizer server is configured to perform operations including receiving a repository configuration request comprising an anonymized data schema, and creating an anonymized data repository clone based on the anonymized data schema. The anonymizer server is also configured to perform operations including anonymizing the PII data to create an anonymized data by applying a one-way data masking, a one-way data morphing, or a combination thereof, and storing the anonymized data in the anonymized data repository clone.

System and method for anonymized data repositories

Disclosed herein are systems and methods for dynamically generating menu items in response to a user selection of text in a scripting interface based on a context associated with the text and metadata associated with the text. Using the techniques disclosed herein, a computing system generates one or more menu items in a pop-up menu in response to a selection (e.g., right-click) of text. The computer system enables user customization of the menu itemsaggregated on the pop-up menu, such that the user customization includes defining the task executed by a menu item in response to being selected from the pop-up menu and defining conditions in which the menu item may be generated in the pop-up menu. In some embodiments, machine-learning techniques may be implemented by the computing system to dynamically identify selection trends and predict suitable menu items to propagate on the pop-up menu.

Dynamic updates for configurable menu items

Provided are instance upgrade techniques useful for upgrading the software of an instance, such as in a multi-instance architecture. The techniques relate to facilitating efficient upgrades to the software of an instance by pausing workflow automations associated with the instances at non-critical stages before the upgrade begins and resuming the workflow automations after the upgrade is complete. The techniques may also include validation workflows to facilitate the upgrade and restart process.

Instance upgrade techniques

A system can include a processor configured to generate a query for a workflow results table, the query including query criteria to include records not having an associated child in the workflow results table and having a value indicating an unsuccessful status. The processor can be further configured to execute the query against the workflow results table to identify at least one activity record having a value indicating an unsuccessful status for the execution of at least one respective workflow activity. The processor can be further configured to generate a graph of workflow activity records between the at least one activity record and a root workflow activity record using context information associated with the workflow activity records. The processor can be further configured to determine an automated remedial action based on the graph of workflow activity records and to execute the remedial action.

System and method for workflow error handling

The present disclosure relates generally to analyzing and comparing computer scripts, and more specifically, to determining semantic similarities between syntactically different scripts. An example computing system includes a memory configured to store a database, wherein the database is configured to store script canonical form (SCF) representations of scripts, and wherein each SCF representation is a topologically-sorted, labeled, directed graph that includes tables and datasets as nodes, actions as edges, and predicates as labels. The system includes a processor configured to execute instructions stored in the memory to cause the computing system to perform actions including: generating a first SCF representation of a first script, and storing the first SCF representation of the first script in the database.

Growing community of inventors

Hyderabad Telangana, India

Prabodh Saha