Servicenow, Inc.

Suki Ai, Inc.

Vmware, Inc.

Badarinarayan Parthasarathi Burli

Sai Chaitanya Ramineni

Harish Chandra Thuwal

Vikas Pradeep Kumar

Vinay Kumar Amble Vasantha

A computer-implemented method is provided that includes receiving a search query and, responsive to the search query, providing one or more textual comments relevant to the search query. This includes tokenizing the search query and calculating a set of query term frequency metrics. A set of records relevant to the search query is then selected, from a persistent storage, based on determined similarities between the query term frequency metrics and frequency metrics determined for the records in the persistent storage. Textual comments within the selected records are associated with usefulness metrics. The textual comments relevant to the search query are selected by selecting those textual comments within the selected records that are associated with usefulness metrics that are within a pre-determined range, e.g., an inter-quartile range for a population of usefulness metrics.

Hierarchical search for improved search relevance

Systems and methods to maintain amends to an annotation as discrete chronological events are disclosed. Exemplary implementations may: obtain a selection of a first annotation template for a first annotation via a client computing platform; generate a root node based on the selection of the first annotation template; obtain a first command to update to the first annotation; append a first update node to the root node responsive to the first command; obtain a second command to update to the first annotation; append a second update node to the first node responsive to the second command; receive an indication to present the first annotation; generate, responsive to the indication, the first annotation by populating the first annotation template included in the root node based on the first node set and in sequential order indicated by the edges; and effectuate presentation of the first annotation.

Systems and methods to maintain amends to an annotation as discrete chronological events

A system could include persistent storage units. Each persistent storage unit could contain: (i) records including descriptions related to technology-related problems, and (ii) term frequency (TF) metrics for tokens within the descriptions. The system could include processors configured to perform operations. The operations may include receiving input text and determining that the input text is subject to a querying procedure. The operations could also include, in response to the determining, identifying a classification for the input text and based on the classification, selecting at least one of the persistent storage units. The operations could further include tokenizing the input text to form input tokens and calculating, for the input tokens, input TF metrics. The operations could additionally include determining similarity metrics between: (i) the input TF metrics, and (ii) TF metrics for records within the at least one persistent storage unit and, based on the similarity metrics, retrieving relevant records.

Autonomous content orchestration

Methods are provided for software build outcome prediction. For example, one method may comprise obtaining feature data associated with a software build, the feature data including one or more of the following: first data identifying a user to perform a modification on a set of one or more software artifacts, second data identifying the set of one or more software artifacts, and third data identifying a set of one or more reviewers to review the modification. The method may also comprise determining first probability data associated with the software build being unsuccessful given the feature data and second probability data associated with the software build being successful given the feature data; and predicting a software build outcome associated with the software build based on the first probability data and the second probability data.

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Bengaluru, India

Badarinarayan Parthasarathi Burli