Servicenow, Inc.

Jebakumar Mathuram Santhosm Swvigaradoss

Ankit Goel

Rajesh Voleti

Rishabh Verma

Satya Sarika Sunkara

Prashant Pandey

Harshvardhan Prasad

John Alan Botica

Patrick Casey

Jason Douglas Aloia

Sakshi Kataria

Laxmi Prasanna Mustala

Michael Dominic Malcangio

Deepak Garg

Molugu Sainithin

Srikar Bakka

Manjeet Singh

Madhusudan Mathihalli

Rao Subrahmanyeswara Surapaneni

Shouvik Goswami

Bharath Padaki

Samantha Santo

Varun Tayal

James Robert Edmonds

Barashnabin Roy

Jeremy Hess

Navakanth Reddy Gandavarapu

Aman Jain

Nidhi Garg

Rao Surapaneni

Data indicative of one or more user inputs directed to a user interface of a conversation designer tool is received. A conversation design is determined based on the data. The conversation design enables a conversation bot to provide a service using a conversation flow specified at least in part by the conversation design. The conversation design specifies in a first human language at least a portion of a message content to be provided by the conversation bot during an execution of the conversation flow. It is identified that an end-user of the conversation bot prefers to converse in a second human language different from the first human language. The message content of the conversation design in the first human language is dynamically translated for the end-user to the second human language.

Dynamic translation for a conversation

One or more associated identifiers are determined based on one or more associated tag types of an interactable element of web content. The determined one or more associated identifiers are associated with the interactable element. Based on the association of the determined one or more associated identifiers with the interactable element, one of the interactable element is matched to a received speech input. An action is performed with respect to the interactable element based on the matching.

Enabling speech interactions on web-based user interfaces

A conversation design is received for a conversation bot that enables the conversation bot to provide a service using a conversation flow specified at least in part by the conversation design. The conversation design specifies in a first human language at least a portion of a message content to be provided by the conversation bot. It is identified that an end-user of the conversation bot prefers to converse in a second human language different from the first human language. In response to a determination that the message content is to be provided by the conversation bot to the end-user, the message content of the conversation design is dynamically translated for the end-user from the first human language to the second human language. The translated message content is provided to the end-user in a message from the conversation bot.

At least service task data and application utilization data are collected. The collected data is analyzed to determine a utilization pattern. Based on the determined utilization pattern, a computer automation opportunity is automatically identified. An automatically generated recommendation of an automation solution among a plurality of automation solution candidates is provided for the identified computer automation opportunity.

Automatic automation recommendation

An input document data is received. The input document data is analyzed to automatically identify one or more response fields and one or more corresponding text labels laid out in the input document data. A conversation bot is automatically configured to provide one or more requests to provide one or more responses corresponding to the one or more response fields, receive the one or more responses, and store the one or more responses in a persistent computer storage.

Automatic conversation bot generation using input form

Web content with a speech interaction user interface capability is provided. Interactable elements of the web content are identified. For each of the interactable elements, one or more associated identifiers are determined and associated with a corresponding interactable element of the identified interactable elements in a data structure. A speech input is received from a user. Using the data structure, one of the interactable elements is matched to the received speech input. An action is automatically performed on the matched interactable element.

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 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

An input document data is received. The document data is analyzed to automatically identify one or more response fields and one or more corresponding text labels laid out in the input document data. An application configured to provide to users the one or more response fields and the one or more corresponding text labels and receive inputs to the response fields from the users that are recorded in a persistent computer storage is automatically generated.

Automatic generation of form application

A database may contain text strings in a preferred language and in one or more other languages. One or more processors may be configured to: generate a graphical user interface containing the text strings in the preferred language and in the other languages, and a control for dynamic translation, wherein a first set of the text strings in the other languages are displayed within text input controls, and wherein a second set of the text strings in the other languages are not displayed within the text input controls; receive an activation indication of the control for dynamic translation; and generate an update to the graphical user interface that includes translations of the first set into the preferred language appearing adjacent to the first set in the other languages, and also translations of the second set into the preferred language replacing the second set in the other languages.

Dynamic translation of graphical user interfaces

The present disclosure relates generally to dynamic translation of text and/or audio data. The client instance hosted by one or more data centers and accessible by one or more remote client networks. In accordance with the present approach, a translation request is received from a user via a client device, wherein the translation request is associated with an untranslated file and a target language. Further, a source language of the untranslated file is identified. Further still, the untranslated file and the target language are outputted to a third party translation service. Even further, a translated file based on the target language, the untranslated file and a source language of the untranslated file is received.

Dynamic translation

A domain separated service catalog may include global catalog items and domain-specific catalog items. Global catalog items may be accessible to all users of a catalog. Catalog item data for global catalog items may be stored in a catalog item data table. Domain separated catalog items may only be accessible within the domain with which they are associated. Catalog item data for domain separated catalog items may also be stored in the catalog item data table. When a user associated with a domain opens the service catalog, data for catalog items may be retrieved from the catalog item data table. The catalog may then be populated with the global catalog items and the domain specific catalog items.

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Hyderabad Telangana, India

Jebakumar Mathuram Santhosm Swvigaradoss