Atlassian Corporation Pty Ltd.

Atlassian US, Inc.

Atlassian Inc.

Geoff Sims

Gaurav Awadhwal

Ahmed Saad

Addo Wondo

Shihab Hassan Hamid

Penyo Atanasov

Matthew David Hunter

Jeff Nelson

Mike Ortman

Michael Fulthorp

Alexander McCarthy

Sreeraj Arakkal

Simon Hugo Robilliard

Hao Ye

Joshua Hansen

Neil Christopher Summers

Edison Rho

Gilmar Jose Alves De Souza, Jr

Gyeun Gyun Rho

Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.

Intelligent selector control for user interfaces

Techniques for identifying similarity between a primary content item and candidate content items are disclosed. The techniques include receiving a similarity request from a client device, the similarity request including contextual data about a primary content item; determining a vector number for the primary content item using the contextual data; identifying candidate content items based on the contextual data; retrieving vector numbers for the identified candidate content items; computing a distance between the vector number of the primary content item and the vector number of each of the candidate content items; ranking the candidate content items based on their computed distance from the primary content item; and selecting at least a subset of the ranked candidate content items as similar content items and communicating the selected subset of the ranked candidate content items to the client device for display on a display of the client device.

Systems and methods for identifying similar electronic content items

A collaboration platform for implicitly determining user collaborations in real time or near real time is disclosed. The collaboration platform monitors event records generated based on user interactions with various team-based tools/collaborative applications to generate collaboration scores that provide a real time picture of a user's interactions with other users, and a user's interactions with content/objects/projects in the tools/collaborative applications. The collaboration platform also monitors user interactions with specific sub-projects to determine at any given time the users actively involved in any given project or sub-project.

System and method of tracking interactions with a networked collaboration platform

Described herein is a computer implemented method for identifying one or more documents of potential relevance to an input query. The method comprises receiving the input query; processing input text from the query to generate an input query vector; accessing document records from a record database, each document record including a document vector; generating a document similarity score in respect of each accessed document, the document similarity score for a given document record being generated using the document vector for the given document record and the input query vector, the document similarity score for a given document record indicating the similarity of the input text to a document that the given document record is in respect of; and identifying one or more potentially relevant document records based on their document similarity scores.

Content discovery systems and methods

Systems and methods for intelligently pre-fetching data for input controls are disclosed. In particular, the disclosed systems and methods predict whether an input control is likely to be edited and only pre-fetches data for the input controls that have a high likelihood of being edited. This way, the presently disclosed systems and methods not only reduce latency in displaying recommended data results for input controls that are likely to be edited but also reduces the number of calls made to the backend server to retrieve the data as the data is not retrieved for all rendered input controls, but only the ones that are likely to be edited.

Systems and methods for selectively prefetching data

Systems, methods and non-transitory computer readable media for detecting incidents are disclosed. The method includes receiving a primary issue creation event record for a primary issue, the event record including a description of the primary issue, and encoding the primary issue into a primary vector number based on the description of the primary issue. The method further includes identifying candidate issues and retrieving vector numbers of the identified candidate issues, computing distances between the primary vector number and each of the candidate vector numbers, and determining whether incident criteria is met based on the computed distances. In addition, the method includes determining that an incident has occurred upon determining that the incident criteria is met and generating an alert.

A product may be associated with a linked document, a link to which is included in a linking document. Associating the linked document with the product may include accessing document linkage data, identifying a relevant linkage record from the linkage data, using a linking document identifier determined from the relevant linkage record to access linking document information in respect of the linking document, processing the linking document information to identify a first product to which the first linking document relates, and associating the first product with the linked document.

Associating product with document using document linkage data

Described herein is a computer implemented method comprising accessing a document, generating a document vector in respect of the document, and generating a sentence vector for each sentence in the document. The method further comprises calculating a sentence similarity score for each sentence in the document which, for a given sentence, is calculated based on a similarity between the sentence vector for the given sentence and the document vector, and identifying one or more representative document sentences for inclusion in a document summary.

Described herein is a computer implemented method for identifying one or more classifications for a particular ticket maintained by an issue tracking system. The method comprises: receiving data in respect of the particular ticket; retrieving cluster data in respect of a set of clusters; calculating cluster membership data in respect of the particular ticket; and identifying, based on the cluster membership data, one or more specific clusters from the set of clusters. For each specific cluster a classification associated with the specific cluster is identified and returned.

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Sydney, Australia

Geoff Sims