Servicenow, Inc.

Hari Krishna Vutukuru

Anushree Randad

Ajesh Sasidharan

Payal Roy

Purvanshi Yadav

Ankit Kumar Das

Giora Tamir

Vasant Balasubramanian

Lal Narayanasamy

Ravindra Bansal

Tamir Segal

Samujjwal Bhandari

Utkarsh Jain

Abhi Sekhar Thyadi

Rama Raghava Reddy Bandi

Prabhat Kuchibhotla

An embodiment may involve storage containing incident logs and mappings between incident logs and vector representations generated by a machine learning (ML) model. The embodiment may further involve one or more processors configured to: receive, from a client device, a classification request corresponding to an additional incident log; transmit, to the ML model, additional values as appearing in the additional incident log, wherein reception of the additional values causes the ML model to generate an additional vector representation of the additional incident log; obtain confidence measurements respectively representing similarities between the additional vector representation and each of the vector representations corresponding to the incident logs; determine, based on the confidence measurements, a set of one or more incident logs that are semantically relevant to the additional incident log; and transmit, to the client device, representations of the one or more incident logs and their corresponding confidence measurements.

Machine-learning based similarity engine

A system includes a processor and memory storing instructions that cause the processor to receive, from a client device, inputs defining associations between one or more control objectives and one more policies, wherein the one or more control objectives define one or more functions to be performed to comply with the one or more policies. The processor may map the one or more policies associated with the one or more control objectives to an application environment and receive, from the client device or a different client device, a change set to an application in the application environment, wherein the change set comprises one or more modifications to the application. The processor may then determine whether the change set adheres to the one or more policies and restrict implementation of the change set in response to determining that the change set does not adhere to the one more policies.

Automated preventative controls in digital workflow

An embodiment may involve storage containing incident logs and mappings between incident logs and vector representations generated by a machine learning (ML) model. The embodiment may further involve one or more processors configured to: receive, from a client device, a request corresponding to an additional incident log; transmit, to the ML model, additional values as appearing in the additional incident log, wherein reception of the additional values causes the ML model to generate an additional vector representation of the additional incident log; obtain confidence measurements respectively representing similarities between the additional vector representation and each of the vector representations corresponding to the incident logs; determine, based on the confidence measurements, a set of one or more incident logs that are semantically relevant to the additional incident log; and transmit, to the client device, representations of the one or more incident logs and their corresponding confidence measurements.

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Hyderabad Telangana, India

Hari Krishna Vutukuru