公开(公告)号：US20240370655A1

公开(公告)日：2024-11-07

申请号：US18656655

申请日：2024-05-07

Applicant: ATLASSIAN PTY LTD. ,  ATLASSIAN US, INC.

Inventor： Karthik MURALIDHARAN ,  Krishna SAI ,  Sri Vardhamanan A ,  Bailur Arjun KINI ,  Shashank Prasad RAO

IPC: G06F40/30 ,  G06F40/205 ,  G06N20/00

Abstract: 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.

公开(公告)号：US20230229852A1

公开(公告)日：2023-07-20

申请号：US18156638

申请日：2023-01-19

Applicant: ATLASSIAN PTY LTD. ,  ATLASSIAN (US) LLC

Inventor： Karthik MURALIDHARAN ,  Shashank Prasad RAO ,  Krishna SAI

IPC: G06F40/166 ,  G06F40/284 ,  G06F40/40

CPC classification number: G06F40/166 ,  G06F40/40 ,  G06F40/284

Abstract: 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.