Atlassian Corporation Pty Ltd.

Atlassian Us, Inc.

Atlassian Inc.

Shashank Prasad Rao

Karthik Muralidharan

Sri Vardhamanan A

Krishna Sai

Vipul Gupta

Akshar Prasad

Viraj Sinha

Bailur Arjun Kini

Bharat Agarwal

Sriram Venkiteswaran

Avinash Agrawal

Methods, apparatuses, systems, and computer program products are disclosed for generating content for presentation at a collaborative content interface based on a captured content source. In example embodiments, an apparatus receives a collaborative content generation request associated with a captured content source defined by one of a source collaborative content document and an external networked location, the collaborative content generation request being associated with a collaborative content document; generates a collaborative content data object associated with the collaborative content document based on a source content data object accessed at the captured content source; stores the collaborative content data object at a system repository such that a collaborative content document data structure defining the collaborative content document includes the collaborative content data object; and causes rendering of a collaborative content interface associated with the collaborative content document based on the collaborative content document data structure and the collaborative content data object.

Apparatuses, computer-implemented methods, and computer program products for generating content for presentation at a collaborative content interface based on a captured content source

Methods, apparatuses, or computer program products provide for generating a service risk analysis score data object. A service risk analysis request associated with an unreleased code object is received. One or more service risk analysis attributes are extracted using a service risk analysis layer based at least in part on the unreleased code object. A service risk analysis score data object is generated using a service risk analysis machine learning model based at least in part on the one or more service risk analysis attributes. The service risk analysis score data object is output.

Apparatuses, methods, and computer program products for ML assisted service risk analysis of unreleased software code

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, a feature combination machine learning model, and a clustering machine learning model.

Machine-learning-based techniques for predictive monitoring of a software application framework

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to accurately and concisely generate one or more action item logs of one or more document data objects. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an action item log of a document data object comprising one or more semantically complete or incomplete units of text data, by generating content segmentation units, determining action item presence predictions, generating action item sets from each content segmentation unit within a candidate action item subset, aggregating the action item sets to create an action item log, and storing the action item log.

Machine learning techniques for semantic processing of structured natural language documents to detect action items

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using alert signatures for the software application that are generated by using at least one of first-order alert classification machine learning models, temporal-sequential alert classification machine learning models, categorical-sequential alert classification machine learning models, alert classification machine learning models, and second-order alert classification machine learning models.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.

Machine-learning-based natural language processing techniques for low-latency document summarization

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient aggregate user-document interaction monitoring in collaborative document server systems. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to perform aggregate user-document interaction monitoring in collaborative document server systems using one or more of collaborative document graph-based user interfaces, collaborative document graph-based interface objects, edge-wise visual effect objects, collaborative document node objects, collaborative document node elements, document transition edge objects, and document transition edge elements.

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Bengaluru, India

Shashank Prasad Rao